{"id":97809,"date":"2026-09-18T18:39:33","date_gmt":"2026-09-18T15:39:33","guid":{"rendered":"https:\/\/twodots.gr\/?p=97809"},"modified":"2026-09-18T18:39:36","modified_gmt":"2026-09-18T15:39:36","slug":"ai-screening-karkinos-stomatos-mobilevitv2","status":"publish","type":"post","link":"https:\/\/twodots.gr\/en\/ai-screening-karkinos-stomatos-mobilevitv2\/","title":{"rendered":"AI screening \u03b3\u03b9\u03b1 \u03ba\u03b1\u03c1\u03ba\u03af\u03bd\u03bf \u03c3\u03c4\u03cc\u03bc\u03b1\u03c4\u03bf\u03c2: \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf \u03bc\u03b9\u03ba\u03c1\u03cc MobileViTv2 \u03ba\u03ad\u03c1\u03b4\u03b9\u03c3\u03b5"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>Answer first:<\/strong> \u03c4\u03bf MobileViTv2 \u03be\u03b5\u03c7\u03ce\u03c1\u03b9\u03c3\u03b5 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b9\u03c3\u03bf\u03b6\u03cd\u03b3\u03b9\u03bf \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 sensitivity, specificity \u03ba\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc \u03b1\u03c0\u03bf\u03c4\u03cd\u03c0\u03c9\u03bc\u03b1\u00b7 \u03cc\u03c7\u03b9 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b1\u03c0\u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 smartphone \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b3\u03bd\u03ce\u03c3\u03b5\u03b9 \u03ba\u03b1\u03c1\u03ba\u03af\u03bd\u03bf \u03c3\u03c4\u03cc\u03bc\u03b1\u03c4\u03bf\u03c2. \u03a4\u03bf AI screening \u03ba\u03b1\u03c1\u03ba\u03af\u03bd\u03bf\u03c5 \u03c3\u03c4\u03cc\u03bc\u03b1\u03c4\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2 \u03b1\u03c6\u03bf\u03c1\u03ac \u03b4\u03c5\u03b1\u03b4\u03b9\u03ba\u03cc triage \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd \u03c9\u03c2 \u00ab\u03cd\u03c0\u03bf\u03c0\u03c4\u03b5\u03c2\u00bb \u03ae \u00ab\u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b5\u03c2\u00bb, \u03bc\u03b5 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac \u03c3\u03b5 \u03c0\u03c1\u03bf\u03c3\u03c9\u03c1\u03b9\u03bd\u03ad\u03c2 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b5\u03c2 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ce\u03bd.<\/p>\n<p>\u03a3\u03c4\u03bf held-out \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd, \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf checkpoint \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 sensitivity 87,4%, specificity 86,5% \u03ba\u03b1\u03b9 negative predictive value 97,2%. \u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ac: \u03c4\u03bf dataset \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03bc\u03af\u03b1 \u03c7\u03ce\u03c1\u03b1, \u03b4\u03b5\u03bd \u03c5\u03c0\u03ae\u03c1\u03c7\u03b5 patient-level \u03c3\u03cd\u03bd\u03b4\u03b5\u03c3\u03b7 \u03c4\u03c9\u03bd \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd, \u03b7 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03c5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c0\u03bf\u03b9\u03bf\u03c4\u03b9\u03ba\u03ac \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03ad\u03b3\u03b9\u03bd\u03b5 \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03ae prospective \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03ba\u03cd\u03c1\u03c9\u03c3\u03b7.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">Contents<\/div>\n<ul>\n<li><a href=\"#ti-metrise\">\u03a4\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b5 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7<\/a><\/li>\n<li><a href=\"#dataset\">\u0388\u03bd\u03b1 \u03b4\u03b5\u03ba\u03b1\u03b5\u03c4\u03ad\u03c2, \u03c0\u03bf\u03bb\u03c5\u03ba\u03b5\u03bd\u03c4\u03c1\u03b9\u03ba\u03cc dataset \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd<\/a><\/li>\n<li><a href=\"#arxitektonikes\">\u0388\u03be\u03b9 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2 \u03b3\u03b9\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf\u03c5\u03c2 \u03c0\u03cc\u03c1\u03bf\u03c5\u03c2<\/a><\/li>\n<li><a href=\"#augmentations\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03b1 augmentations \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03cc \u03bd\u03cc\u03b7\u03bc\u03b1<\/a><\/li>\n<li><a href=\"#class-imbalance\">\u0397 \u03b1\u03bd\u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03af\u03b1 \u03ba\u03bb\u03ac\u03c3\u03b5\u03c9\u03bd \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03c4\u03bf\u03bd \u03c3\u03c4\u03cc\u03c7\u03bf \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2<\/a><\/li>\n<li><a href=\"#apodosi\">\u03a4\u03b9 \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ac \u03c4\u03bf MobileViTv2<\/a><\/li>\n<li><a href=\"#distillation\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03c0\u03bf\u03bb\u03c5\u03c0\u03bb\u03bf\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b4\u03b5\u03bd \u03ba\u03ad\u03c1\u03b4\u03b9\u03c3\u03b5<\/a><\/li>\n<li><a href=\"#ermineia\">\u0397 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03c5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ad\u03b4\u03c9\u03c3\u03b5 \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7<\/a><\/li>\n<li><a href=\"#stress-tests\">\u03a4\u03b1 stress tests \u03b1\u03c0\u03bf\u03ba\u03ac\u03bb\u03c5\u03c8\u03b1\u03bd \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf failure mode<\/a><\/li>\n<li><a href=\"#on-device\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac on-device<\/a><\/li>\n<li><a href=\"#governance\">\u0391\u03c0\u03cc \u03c4\u03bf paper \u03c3\u03b5 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 product governance<\/a><\/li>\n<li><a href=\"#symperasma\">\u03a4\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 AI \u03ba\u03b1\u03b9 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"ti-metrise\">\u03a4\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b5 \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7<\/h2>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u00abRobust Lightweight Deep Learning Models for Oral Cancer Screening\u00bb \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03b9 \u03b1\u03bd \u03ad\u03bd\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03cc\u03c1\u03b1\u03c3\u03b7\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03be\u03b5\u03b9 triage \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd \u03c3\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u03c2 \u03ba\u03bf\u03b9\u03bb\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b1\u03c0\u03cc smartphones. \u03a4\u03bf \u03b5\u03c1\u03ce\u03c4\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03cc \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03cc: \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03ad\u03bd\u03b1 \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03bc\u03b5 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 compute \u03bd\u03b1 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03b1\u03c1\u03b1\u03c0\u03bf\u03bc\u03c0\u03ae, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 cloud \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae;<\/p>\n<p>\u0397 \u03ad\u03be\u03bf\u03b4\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03c5\u03b1\u03b4\u03b9\u03ba\u03ae. \u0397 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b1 \u00ab\u03cd\u03c0\u03bf\u03c0\u03c4\u03b7\u00bb \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03b2\u03bb\u03ac\u03b2\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03bd \u03c0\u03b1\u03c1\u03b1\u03c0\u03bf\u03bc\u03c0\u03ae, \u03b1\u03c0\u03cc \u03b4\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ac \u03ba\u03b1\u03ba\u03bf\u03ae\u03b8\u03b5\u03b9\u03c2 \u03b4\u03b9\u03b1\u03c4\u03b1\u03c1\u03b1\u03c7\u03ad\u03c2 \u03ad\u03c9\u03c2 \u03ba\u03b1\u03c1\u03ba\u03b9\u03bd\u03ce\u03bc\u03b1\u03c4\u03b1, \u03b5\u03bd\u03ce \u03b7 \u00ab\u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b7\u00bb \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03c6\u03c5\u03c3\u03b9\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b1\u03bd\u03b1\u03c4\u03bf\u03bc\u03af\u03b1, \u03c0\u03b1\u03c1\u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03ba\u03b1\u03bb\u03bf\u03ae\u03b8\u03b5\u03b9\u03c2 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03c3\u03b5\u03b9\u03c2. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03b9\u03c3\u03bf\u03b4\u03c5\u03bd\u03b1\u03bc\u03b5\u03af \u03bc\u03b5 \u03b9\u03c3\u03c4\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b4\u03b9\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7, risk stratification \u03b1\u03c3\u03b8\u03b5\u03bd\u03bf\u03cd\u03c2 \u03ae \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03b5\u03b9\u03b4\u03b9\u03ba\u03bf\u03cd.<\/p>\n<p>\u0397 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03ad\u03c7\u03b5\u03b9 \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1 \u03ba\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c4\u03bf product messaging. \u03a3\u03b5 AI \u03c5\u03c8\u03b7\u03bb\u03bf\u03cd \u03c1\u03af\u03c3\u03ba\u03bf\u03c5, \u03b7 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03b1\u03c6\u03ae \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03b1\u03c5\u03c4\u03cc \u03c0\u03bf\u03c5 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03b8\u03b7\u03ba\u03b5. \u0397 \u03af\u03b4\u03b9\u03b1 \u03b1\u03c1\u03c7\u03ae \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd <a href=\"https:\/\/twodots.gr\/ai-trisdiastati-aktinologia-report-den-arkei\/\">AI \u03b3\u03b9\u03b1 \u03c4\u03c1\u03b9\u03c3\u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c4\u03b7 \u03b1\u03ba\u03c4\u03b9\u03bd\u03bf\u03bb\u03bf\u03b3\u03af\u03b1<\/a>: \u03ad\u03bd\u03b1 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf output \u03ae report \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03c5 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7.<\/p>\n<div class=\"td-comparison td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid--three\">\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">\u0395\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1<\/p>\n<p>Image-level sensitivity, specificity \u03ba\u03b1\u03b9 NPV \u03c3\u03b5 retrospective held-out \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03bc\u03b5 \u03c0\u03c1\u03bf\u03c3\u03c9\u03c1\u03b9\u03bd\u03ad\u03c2 \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b5\u03c2 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ce\u03bd.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u039c\u03b5\u03c4\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5<\/span><span class=\"td-badge\">Retrospective<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<p class=\"td-comparison-title\">Engineering readiness<\/p>\n<p>\u039c\u03b9\u03ba\u03c1\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03b1\u03c0\u03bb\u03bf\u03cd\u03c3\u03c4\u03b5\u03c1\u03bf pipeline \u03ba\u03b1\u03b9 stress tests\u00b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ad\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c5.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u03a5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7<\/span><span class=\"td-badge\">Device tests<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<p class=\"td-comparison-title\">\u039a\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae readiness<\/p>\n<p>\u0391\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 prospective \u03b5\u03c0\u03b9\u03ba\u03cd\u03c1\u03c9\u03c3\u03b7, patient-level splits, \u03c0\u03bf\u03bb\u03bb\u03bf\u03cd\u03c2 annotators \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae \u03c1\u03bf\u03ae \u03c0\u03b1\u03c1\u03b1\u03c0\u03bf\u03bc\u03c0\u03ae\u03c2.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03af\u03c7\u03b8\u03b7\u03ba\u03b5<\/span><span class=\"td-badge\">Human oversight<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"dataset\">\u0388\u03bd\u03b1 \u03b4\u03b5\u03ba\u03b1\u03b5\u03c4\u03ad\u03c2, \u03c0\u03bf\u03bb\u03c5\u03ba\u03b5\u03bd\u03c4\u03c1\u03b9\u03ba\u03cc dataset \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd<\/h2>\n<p>\u039f\u03b9 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03c0\u03c1\u03bf\u03ae\u03bb\u03b8\u03b1\u03bd \u03b1\u03c0\u03cc \u03c0\u03c1\u03bf\u03b3\u03c1\u03ac\u03bc\u03bc\u03b1\u03c4\u03b1 \u03c4\u03b7\u03c2 Biocon Foundation \u03c3\u03b5 \u03b2\u03cc\u03c1\u03b5\u03b9\u03b5\u03c2, \u03b2\u03bf\u03c1\u03b5\u03b9\u03bf\u03b1\u03bd\u03b1\u03c4\u03bf\u03bb\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03bd\u03cc\u03c4\u03b9\u03b5\u03c2 \u03c0\u03bf\u03bb\u03b9\u03c4\u03b5\u03af\u03b5\u03c2 \u03c4\u03b7\u03c2 \u0399\u03bd\u03b4\u03af\u03b1\u03c2, \u03b1\u03c0\u03cc \u03c4\u03bf 2011 \u03ad\u03c9\u03c2 \u03c4\u03bf 2023. Frontline \u03b5\u03c1\u03b3\u03b1\u03b6\u03cc\u03bc\u03b5\u03bd\u03bf\u03b9 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b1\u03bd \u03ba\u03b1\u03c4\u03b1\u03bd\u03b1\u03bb\u03c9\u03c4\u03b9\u03ba\u03ac smartphones HTC, Motorola, Xiaomi \u03ba\u03b1\u03b9 Samsung \u03bc\u03b5 \u03ba\u03ac\u03bc\u03b5\u03c1\u03b5\u03c2 5 \u03ad\u03c9\u03c2 16 megapixel. \u039f\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03bf\u03af \u03b1\u03b9\u03c3\u03b8\u03b7\u03c4\u03ae\u03c1\u03b5\u03c2, \u03bf \u03c6\u03c9\u03c4\u03b9\u03c3\u03bc\u03cc\u03c2, \u03b7 \u03b5\u03c3\u03c4\u03af\u03b1\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03b3\u03c9\u03bd\u03af\u03b5\u03c2 \u03bb\u03ae\u03c8\u03b7\u03c2 \u03b4\u03b5\u03bd \u03b1\u03c6\u03b1\u03b9\u03c1\u03ad\u03b8\u03b7\u03ba\u03b1\u03bd \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1\u00b7 \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03b1\u03bd \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5.<\/p>\n<p>\u03a4\u03bf \u03b1\u03c1\u03c7\u03b9\u03ba\u03cc \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b5\u03af\u03c7\u03b5 31.601 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2. \u039c\u03b5\u03c4\u03ac \u03c4\u03b7\u03bd \u03b1\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03b7 126 \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c0\u03b1\u03bd\u03bf\u03bc\u03bf\u03b9\u03cc\u03c4\u03c5\u03c0\u03c9\u03bd \u03b4\u03b9\u03c0\u03bb\u03bf\u03c4\u03cd\u03c0\u03c9\u03bd \u03ba\u03b1\u03b9 \u03bc\u03b7 \u03b4\u03b9\u03b1\u03b3\u03bd\u03c9\u03c3\u03c4\u03b9\u03ba\u03ce\u03bd \u03bb\u03ae\u03c8\u03b5\u03c9\u03bd, \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03b9\u03bd\u03b1\u03bd 29.574 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2: 4.887 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b5\u03c2 \u03ba\u03b1\u03b9 24.687 \u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b5\u03c2. \u039f \u03b4\u03b9\u03b1\u03c7\u03c9\u03c1\u03b9\u03c3\u03bc\u03cc\u03c2 60%-20%-20% \u03ad\u03b4\u03c9\u03c3\u03b5 17.744 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03b3\u03b9\u03b1 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7, 5.915 \u03b3\u03b9\u03b1 validation \u03ba\u03b1\u03b9 5.915 \u03b3\u03b9\u03b1 \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc test.<\/p>\n<p>\u0397 \u03c0\u03bf\u03b9\u03ba\u03b9\u03bb\u03af\u03b1 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ce\u03bd \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03c1\u03b5\u03b1\u03bb\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b5\u03be\u03b1\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf geographic domain shift. \u038c\u03c0\u03c9\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/geographic-domain-shift-ai-modelo-se-nees-agores\/\">\u03c0\u03ce\u03c2 \u03ad\u03bd\u03b1 AI \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c4\u03b1\u03be\u03b9\u03b4\u03b5\u03cd\u03b5\u03b9 \u03c3\u03b5 \u03bd\u03ad\u03b5\u03c2 \u03b1\u03b3\u03bf\u03c1\u03ad\u03c2<\/a>, \u03b7 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd \u03c0\u03bb\u03b7\u03b8\u03c5\u03c3\u03bc\u03cc \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03c3\u03c5\u03bb\u03bb\u03bf\u03b3\u03ae\u03c2 \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03af\u03b4\u03b9\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c3\u03b5 \u03ac\u03bb\u03bb\u03b7 \u03c7\u03ce\u03c1\u03b1, \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03ae \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ce\u03bd.<\/p>\n<aside class=\"td-article-note\"><strong>\u0397 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03b4\u03c5\u03bd\u03b1\u03bc\u03af\u03b1 \u03c4\u03bf\u03c5 split:<\/strong> \u03bb\u03cc\u03b3\u03c9 \u03b1\u03bd\u03c9\u03bd\u03c5\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03b4\u03b5\u03bd \u03c5\u03c0\u03ae\u03c1\u03c7\u03b5 \u03c3\u03cd\u03bd\u03b4\u03b5\u03c3\u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2 \u03bc\u03b5 \u03b1\u03c3\u03b8\u03b5\u03bd\u03ae. \u03a4\u03bf paper \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03b5\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03af\u03c3\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03c4\u03bf\u03c5 \u03af\u03b4\u03b9\u03bf\u03c5 \u03b1\u03c4\u03cc\u03bc\u03bf\u03c5 \u03b2\u03c1\u03ad\u03b8\u03b7\u03ba\u03b1\u03bd \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac splits. \u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03b1\u03c6\u03bf\u03c1\u03ac \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, \u03cc\u03c7\u03b9 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03bf\u03c5\u03c2 \u03b1\u03c3\u03b8\u03b5\u03bd\u03b5\u03af\u03c2.<\/aside>\n<h2 id=\"arxitektonikes\">\u0388\u03be\u03b9 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2 \u03b3\u03b9\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf\u03c5\u03c2 \u03c0\u03cc\u03c1\u03bf\u03c5\u03c2<\/h2>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c0\u03b5\u03c1\u03b9\u03cc\u03c1\u03b9\u03c3\u03b1\u03bd \u03c4\u03b7\u03bd \u03b1\u03bd\u03b1\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7 \u03c3\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc 7,5 \u03b5\u03ba\u03b1\u03c4. \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2. \u03a3\u03c5\u03bd\u03ad\u03ba\u03c1\u03b9\u03bd\u03b1\u03bd \u03c4\u03b1 CNN EfficientNetV2-B0, MobileNetV3-Large \u03ba\u03b1\u03b9 NASNet-Mobile, \u03c4\u03bf\u03bd transformer DeiT-Ti \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03c5\u03b2\u03c1\u03b9\u03b4\u03b9\u03ba\u03ac MobileViTv2 \u03ba\u03b1\u03b9 EdgeNeXt-S. \u038c\u03bb\u03b5\u03c2 \u03bf\u03b9 \u03b4\u03b9\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03c3\u03b5\u03b9\u03c2 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b1\u03bd \u03bc\u03b5 ImageNet pretraining, \u03b3\u03b9\u03b1 50 epochs \u03ba\u03b1\u03b9 batch size 32, \u03b5\u03bd\u03ce \u03ba\u03ac\u03b8\u03b5 \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c6\u03b8\u03b7\u03ba\u03b5 \u03b4\u03ad\u03ba\u03b1 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac seeds.<\/p>\n<p>\u03a4\u03bf MobileViTv2-1.0 \u03b5\u03af\u03c7\u03b5 4,9 \u03b5\u03ba\u03b1\u03c4. \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03af\u03c3\u03bf\u03b4\u03bf 256\u00d7256. \u03a3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ae \u03b5\u03be\u03b1\u03b3\u03c9\u03b3\u03ae \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ce\u03bd \u03bc\u03ad\u03c3\u03c9 convolutions \u03bc\u03b5 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03bf context \u03bc\u03ad\u03c3\u03c9 attention. \u0397 separable self-attention \u03c4\u03bf\u03c5 \u03ad\u03c7\u03b5\u03b9 \u03b3\u03c1\u03b1\u03bc\u03bc\u03b9\u03ba\u03ae \u03c0\u03bf\u03bb\u03c5\u03c0\u03bb\u03bf\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c9\u03c2 \u03c0\u03c1\u03bf\u03c2 \u03c4\u03b1 tokens, \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c3\u03b5 \u03c3\u03c7\u03ad\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03ba\u03bb\u03b1\u03c3\u03b9\u03ba\u03ae \u03c4\u03b5\u03c4\u03c1\u03b1\u03b3\u03c9\u03bd\u03b9\u03ba\u03ae self-attention.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-title\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 MobileViTv2 pipeline<\/p>\n<p class=\"td-chart-subtitle\">\u039f\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03ae \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 smartphone.<\/p>\n<\/div>\n<div class=\"td-chart-body\">\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">29.574 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2<\/span><span class=\"td-metric-label\">\u03bc\u03b5\u03c4\u03ac \u03c4\u03bf\u03bd \u03ba\u03b1\u03b8\u03b1\u03c1\u03b9\u03c3\u03bc\u03cc \u03c4\u03bf\u03c5 retrospective dataset<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">4,9 \u03b5\u03ba\u03b1\u03c4.<\/span><span class=\"td-metric-label\">\u03c0\u03b1\u03c1\u03ac\u03bc\u03b5\u03c4\u03c1\u03bf\u03b9 \u03c3\u03c4\u03bf MobileViTv2-1.0<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">17 MB<\/span><span class=\"td-metric-label\">\u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03bf \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03c4\u03c9\u03bd \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03bc\u03ad\u03bd\u03c9\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">97,2%<\/span><span class=\"td-metric-label\">NPV \u03c4\u03bf\u03c5 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c5 checkpoint \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf test set<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0397 \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03c5\u03b3\u03b5\u03af\u03b1: \u03b7 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03bb\u03ad\u03b3\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 \u03c4\u03bf \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1\u03c2. \u03a3\u03b5 edge deployments, bandwidth, latency, \u03ba\u03b1\u03c4\u03b1\u03bd\u03ac\u03bb\u03c9\u03c3\u03b7 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1\u03c2, privacy \u03ba\u03b1\u03b9 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 offline \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 model requirement, \u03cc\u03c7\u03b9 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03c3\u03c4\u03af\u03b8\u03b5\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03c4\u03ad\u03bb\u03bf\u03c2.<\/p>\n<h2 id=\"augmentations\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03b1 augmentations \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03cc \u03bd\u03cc\u03b7\u03bc\u03b1<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03bf\u03ba\u03af\u03bc\u03b1\u03c3\u03b5 zoom, \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03c7\u03c1\u03ce\u03bc\u03b1\u03c4\u03bf\u03c2, flips, \u03bc\u03b9\u03ba\u03c1\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c4\u03c1\u03bf\u03c6\u03ad\u03c2 \u03ba\u03b1\u03b9 coarse masks \u03b1\u03c0\u03cc \u03c4\u03bf Segment Anything Model. \u039f\u03b9 \u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03bf\u03af \u03bc\u03b5\u03c4\u03b1\u03c3\u03c7\u03b7\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af \u2014flips \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c4\u03c1\u03bf\u03c6\u03ad\u03c2 \u03b1\u03c0\u03cc -10\u00b0 \u03ad\u03c9\u03c2 10\u00b0\u2014 \u03ad\u03b4\u03c9\u03c3\u03b1\u03bd \u03c4\u03b7\u03bd \u03c0\u03b9\u03bf \u03c3\u03c5\u03bd\u03b5\u03c0\u03ae \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7. \u03a4\u03bf colour jitter \u03b1\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1 \u03c5\u03c0\u03bf\u03b2\u03ac\u03b8\u03bc\u03b9\u03c3\u03b5 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ad\u03c2 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03bf \u03c7\u03c1\u03ce\u03bc\u03b1 \u03bc\u03b9\u03b1\u03c2 \u03b5\u03c1\u03c5\u03b8\u03c1\u03ae\u03c2 \u03ae \u03bb\u03b5\u03c5\u03ba\u03ae\u03c2 \u03b2\u03bb\u03ac\u03b2\u03b7\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ac \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03c3\u03ae\u03bc\u03b1.<\/p>\n<p>\u03a4\u03bf SAM \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b5 \u03bc\u03cc\u03bd\u03bf offline \u03c3\u03c4\u03b7\u03bd \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7, \u03cc\u03c7\u03b9 \u03c3\u03c4\u03bf inference. \u03a3\u03b5 zero-shot \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03af\u03b1, \u03bf\u03b9 coarse masks \u03ad\u03ba\u03c1\u03c5\u03b2\u03b1\u03bd \u03c3\u03c5\u03c7\u03bd\u03ac \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03b7\u03c2 \u03c3\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u03c2 \u03ba\u03bf\u03b9\u03bb\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u0397 \u03b1\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03ae \u03c4\u03bf\u03c5 \u03b1\u03c0\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03c4\u03bf pipeline \u03ba\u03b1\u03b9 \u03b1\u03c0\u03ad\u03c6\u03c5\u03b3\u03b5 \u03bc\u03b9\u03b1 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b7\u03c4\u03b9\u03ba\u03ae \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03c7\u03c9\u03c1\u03af\u03c2 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2.<\/p>\n<p>\u0394\u03cd\u03bf no-reference \u03b4\u03b5\u03af\u03ba\u03c4\u03b5\u03c2 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2, NIQE \u03ba\u03b1\u03b9 CLIP-IQA, \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b3\u03b9\u03b1 reweighting \u03c4\u03b7\u03c2 loss. \u0394\u03b5\u03bd \u03c0\u03c1\u03bf\u03ad\u03ba\u03c5\u03c8\u03b5 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1. \u0388\u03bd\u03b1\u03c2 \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc\u03c2 \u03b4\u03b5\u03af\u03ba\u03c4\u03b7\u03c2 \u03b1\u03b9\u03c3\u03b8\u03b7\u03c4\u03b9\u03ba\u03ae\u03c2 \u03ae \u00ab\u03c6\u03c5\u03c3\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2\u00bb \u03c4\u03b7\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2 \u03b4\u03b5\u03bd \u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03b5\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03b1\u03c4\u03ad\u03bb\u03b5\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b4\u03b9\u03b1\u03b3\u03bd\u03c9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7. \u03a4\u03bf product lesson \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 proxy metric \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03bf\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03af\u03bd\u03b4\u03c5\u03bd\u03bf \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03bc\u03b5 \u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03ba\u03b1\u03b8\u03b1\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1.<\/p>\n<h2 id=\"class-imbalance\">\u0397 \u03b1\u03bd\u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03af\u03b1 \u03ba\u03bb\u03ac\u03c3\u03b5\u03c9\u03bd \u03ac\u03bb\u03bb\u03b1\u03be\u03b5 \u03c4\u03bf\u03bd \u03c3\u03c4\u03cc\u03c7\u03bf \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7\u03c2<\/h2>\n<p>\u0397 \u03b1\u03bd\u03b1\u03bb\u03bf\u03b3\u03af\u03b1 \u03ae\u03c4\u03b1\u03bd \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03bc\u03af\u03b1 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b7 \u03c0\u03c1\u03bf\u03c2 \u03c0\u03ad\u03bd\u03c4\u03b5 \u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b5\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2. \u03a3\u03b5 \u03c4\u03ad\u03c4\u03bf\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03ad\u03bd\u03b1 \u03c5\u03c8\u03b7\u03bb\u03cc \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc accuracy \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03c1\u03cd\u03c8\u03b5\u03b9 \u03b1\u03c0\u03bf\u03c4\u03c5\u03c7\u03af\u03b1 \u03c3\u03c4\u03b7 \u03bc\u03b5\u03b9\u03bf\u03c8\u03b7\u03c6\u03b9\u03ba\u03ae \u03ba\u03bb\u03ac\u03c3\u03b7. \u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03bf\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03bd\u03b1\u03bd random undersampling, random oversampling, weighted cross-entropy, focal loss \u03ba\u03b1\u03b9 balanced Mix-up.<\/p>\n<p>\u03a4\u03bf undersampling \u03b1\u03cd\u03be\u03b7\u03c3\u03b5 \u03c4\u03b7 sensitivity \u03b1\u03bb\u03bb\u03ac \u03c0\u03ad\u03c4\u03b1\u03be\u03b5 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03b5\u03bb\u03b1\u03c6\u03c1\u03ac \u03c4\u03b7 specificity. \u03a4\u03bf oversampling \u03ae\u03c4\u03b1\u03bd \u03c0\u03b9\u03bf \u03c3\u03c5\u03bd\u03c4\u03b7\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b9\u03cc\u03b3\u03ba\u03c9\u03c3\u03b5 \u03c4\u03bf training set. \u03a4\u03bf balanced Mix-up \u03b1\u03bd\u03ad\u03b2\u03b1\u03c3\u03b5 \u03c4\u03b7 sensitivity \u03c3\u03b5 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2, \u03cc\u03bc\u03c9\u03c2 \u03c0\u03c1\u03bf\u03ba\u03ac\u03bb\u03b5\u03c3\u03b5 \u03b1\u03c0\u03cc\u03c4\u03bf\u03bc\u03b7 \u03c0\u03c4\u03ce\u03c3\u03b7 \u03c4\u03b7\u03c2 specificity \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03c8\u03b5\u03c5\u03b4\u03ce\u03c2 \u03b8\u03b5\u03c4\u03b9\u03ba\u03ac.<\/p>\n<p>\u0397 weighted cross-entropy \u03ad\u03b4\u03c9\u03c3\u03b5 \u03c4\u03bf \u03c0\u03b9\u03bf \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03b9\u03c3\u03bf\u03b6\u03cd\u03b3\u03b9\u03bf. \u03a4\u03b1 \u03b2\u03ac\u03c1\u03b7 \u03bf\u03c1\u03af\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03c3\u03b5 0,60 \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b7 \u03ba\u03b1\u03b9 3,03 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03cd\u03c0\u03bf\u03c0\u03c4\u03b7 \u03ba\u03bb\u03ac\u03c3\u03b7, \u03b2\u03ac\u03c3\u03b5\u03b9 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03c1\u03bf\u03c6\u03b7\u03c2 \u03c3\u03c5\u03c7\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c3\u03c4\u03bf training split. \u0397 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b5 \u03c4\u03b7 sensitivity \u03c3\u03b5 \u03cc\u03bb\u03b5\u03c2 \u03c4\u03b9\u03c2 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2 \u03c7\u03c9\u03c1\u03af\u03c2 \u03c4\u03bf \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03bf training burden \u03c4\u03bf\u03c5 oversampling \u03ba\u03b1\u03b9 \u03c7\u03c9\u03c1\u03af\u03c2 \u03c4\u03b7\u03bd \u03b1\u03c0\u03ce\u03bb\u03b5\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b4\u03b5\u03b9\u03b3\u03bc\u03ac\u03c4\u03c9\u03bd \u03c4\u03bf\u03c5 undersampling.<\/p>\n<h2 id=\"apodosi\">\u03a4\u03b9 \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ac \u03c4\u03bf MobileViTv2<\/h2>\n<p>\u039f \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc\u03c2 MobileViTv2, \u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03ce\u03bd augmentations \u03ba\u03b1\u03b9 weighted cross-entropy \u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c3\u03b5 \u03c0\u03b5\u03bd\u03c4\u03b1\u03c0\u03bb\u03ae cross-validation. \u0397 \u03bc\u03ad\u03c3\u03b7 sensitivity \u03ae\u03c4\u03b1\u03bd 83,2% \u03bc\u03b5 \u03c4\u03c5\u03c0\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03ba\u03bb\u03b9\u03c3\u03b7 1,5 \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03b9\u03b1\u03af\u03b1\u03c2 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b7 \u03bc\u03ad\u03c3\u03b7 specificity 86,0% \u03bc\u03b5 \u03b1\u03c0\u03cc\u03ba\u03bb\u03b9\u03c3\u03b7 0,8. \u03a4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf checkpoint \u03c4\u03bf\u03c5 \u03c0\u03c1\u03ce\u03c4\u03bf\u03c5 fold \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 sensitivity 87,41% \u03ba\u03b1\u03b9 specificity 86,49%.<\/p>\n<p>\u03a3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf held-out image set, \u03c4\u03bf checkpoint \u03b5\u03af\u03c7\u03b5 negative predictive value 97,2% \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c4\u03c9\u03bd \u03b5\u03c4\u03b9\u03ba\u03b5\u03c4\u03ce\u03bd \u03b5\u03b9\u03b4\u03b9\u03ba\u03ce\u03bd. \u0397 NPV \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03ba\u03c1\u03ac\u03c4\u03b7\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03b8\u03b5\u03c4\u03b9\u03ba\u03ae\u03c2 \u03ba\u03bb\u03ac\u03c3\u03b7\u03c2 \u03c3\u03c4\u03bf\u03bd \u03c0\u03bb\u03b7\u03b8\u03c5\u03c3\u03bc\u03cc \u03cc\u03c0\u03bf\u03c5 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ad\u03c4\u03b1\u03b9. \u0394\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03b5\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03ba\u03ac \u03c3\u03b5 \u03ac\u03bb\u03bb\u03b7 \u03c7\u03ce\u03c1\u03b1, \u03b7\u03bb\u03b9\u03ba\u03b9\u03b1\u03ba\u03ae \u03bf\u03bc\u03ac\u03b4\u03b1, \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03c1\u03bf\u03ae \u03ae \u03c3\u03c5\u03c7\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bd\u03cc\u03c3\u03bf\u03c5.<\/p>\n<p>\u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b5\u03af\u03c7\u03b5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ae \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03b3\u03b9\u03b1 triage \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf retrospective \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1. \u0394\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03c3\u03c4\u03bf paper patient-level sensitivity, \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03cc test set, randomized \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c1\u03bf\u03ce\u03bd \u03c6\u03c1\u03bf\u03bd\u03c4\u03af\u03b4\u03b1\u03c2 \u03ae \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ce\u03bd \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ce\u03bd \u03b5\u03ba\u03b2\u03ac\u03c3\u03b5\u03c9\u03bd.<\/p>\n<p>\u0397 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae\u03c2 \u03c3\u03b5 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03cc \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ac \u03b2\u03ad\u03bb\u03c4\u03b9\u03c3\u03c4\u03b7. \u0397 \u03ad\u03c1\u03b5\u03c5\u03bd\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd <a href=\"https:\/\/twodots.gr\/athena-ai-ehr-neural-architecture-search\/\">ATHENA \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae\u03c2 \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03bd\u03bf\u03c3\u03bf\u03ba\u03bf\u03bc\u03b5\u03af\u03c9\u03bd<\/a> \u03c6\u03c9\u03c4\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03b1\u03c0\u03cc \u03ac\u03bb\u03bb\u03b7 \u03b3\u03c9\u03bd\u03af\u03b1: \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bc\u03b5 \u03c4\u03b7\u03bd \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03b3\u03b5\u03bd\u03af\u03ba\u03b5\u03c5\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2.<\/p>\n<h2 id=\"distillation\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03c0\u03bf\u03bb\u03c5\u03c0\u03bb\u03bf\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b4\u03b5\u03bd \u03ba\u03ad\u03c1\u03b4\u03b9\u03c3\u03b5<\/h2>\n<p>\u039f\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03b4\u03bf\u03ba\u03af\u03bc\u03b1\u03c3\u03b1\u03bd knowledge distillation \u03b1\u03c0\u03cc \u03c4\u03c1\u03af\u03b1 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b1 teacher models: ViT-B\/16 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 86 \u03b5\u03ba\u03b1\u03c4. \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd, EfficientNetV2-L \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 118 \u03b5\u03ba\u03b1\u03c4. \u03ba\u03b1\u03b9 Swin-B \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 88 \u03b5\u03ba\u03b1\u03c4. \u03a4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b9\u03c3\u03bf\u03c1\u03c1\u03bf\u03c0\u03b7\u03bc\u03ad\u03bd\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03bc\u03b5 ViT-B\/16 \u03ae\u03c4\u03b1\u03bd \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac \u03c4\u03b7\u03bd \u03ac\u03bc\u03b5\u03c3\u03b7 \u03b5\u03ba\u03c0\u03b1\u03af\u03b4\u03b5\u03c5\u03c3\u03b7 \u03cc\u03c4\u03b1\u03bd \u03c3\u03c5\u03bd\u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2.<\/p>\n<p>\u039f\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b4\u03b9\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03c3\u03b5\u03b9\u03c2 \u03ae\u03c4\u03b1\u03bd \u03b1\u03c3\u03c4\u03b1\u03b8\u03b5\u03af\u03c2. \u039c\u03b5 EfficientNetV2-L \u03ba\u03b1\u03b9 100% distillation, \u03b7 \u03bc\u03ad\u03c3\u03b7 sensitivity \u03ad\u03c0\u03b5\u03c3\u03b5 \u03c3\u03c4\u03bf 38,16% \u03bc\u03b5 \u03c0\u03bf\u03bb\u03cd \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7. \u039c\u03b5 Swin-B \u03ba\u03b1\u03b9 100% distillation, \u03b7 sensitivity \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03c3\u03c4\u03bf 86,43%, \u03b1\u03bb\u03bb\u03ac \u03b7 specificity \u03ad\u03c0\u03b5\u03c3\u03b5 \u03c3\u03c4\u03bf 62,65%. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c3\u03c5\u03bd\u03b4\u03ad\u03bf\u03c5\u03bd \u03c4\u03b1 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ae\u03bc\u03b1\u03c4\u03b1 \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 inductive bias, \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b5\u03c0\u03b1\u03c1\u03ba\u03ad\u03c2 anchoring \u03c3\u03c4\u03b1 ground-truth labels.<\/p>\n<p>\u03a4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf distillation \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ac \u03ac\u03c7\u03c1\u03b7\u03c3\u03c4\u03bf. \u03a3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b5\u03b4\u03ce \u03b4\u03b5\u03bd \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b5 \u03c4\u03b7\u03bd \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03b7 \u03c0\u03bf\u03bb\u03c5\u03c0\u03bb\u03bf\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1. \u03a3\u03b5 production \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc, \u03ba\u03ac\u03b8\u03b5 segmentation \u03b2\u03ae\u03bc\u03b1, teacher network \u03ae quality proxy \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7 \u03b8\u03ad\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03bc\u03b5 ablation, failure analysis \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03c3\u03c4\u03bf risk-relevant metric.<\/p>\n<h2 id=\"ermineia\">\u0397 \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03c5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ad\u03b4\u03c9\u03c3\u03b5 \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7<\/h2>\n<p>\u0393\u03b9\u03b1 \u03c4\u03bf\u03bd convolutional \u03ba\u03bb\u03ac\u03b4\u03bf \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b5 GradCAM++ \u03ba\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c4\u03b1 transformer blocks attention rollout \u03c3\u03c4\u03b9\u03c2 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b5\u03c2 16, 64 \u03ba\u03b1\u03b9 256. \u039f\u03b9 \u03bf\u03c0\u03c4\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9\u03c2 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c7\u03c9\u03c1\u03b9\u03ba\u03ae \u03b5\u03c5\u03b8\u03c5\u03b3\u03c1\u03ac\u03bc\u03bc\u03b9\u03c3\u03b7 \u03c4\u03c9\u03bd \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03c9\u03bd \u03bc\u03b5 \u03c4\u03b9\u03c2 \u03b5\u03c0\u03b9\u03c3\u03b7\u03bc\u03ac\u03bd\u03c3\u03b5\u03b9\u03c2 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ce\u03bd \u03ba\u03b1\u03b9 \u03b5\u03c3\u03c4\u03af\u03b1\u03c3\u03b7 \u03c3\u03c4\u03b7\u03bd \u03c0\u03b5\u03c1\u03b9\u03bf\u03c7\u03ae \u03c4\u03b7\u03c2 \u03c3\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u03c2 \u03ba\u03bf\u03b9\u03bb\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2.<\/p>\n<p>\u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03b9\u03bd\u03b5 \u03c0\u03bf\u03b9\u03bf\u03c4\u03b9\u03ba\u03ae. \u03a5\u03c0\u03ae\u03c1\u03c7\u03b1\u03bd \u03bc\u03cc\u03bd\u03bf 244 specialist-segmented masks, \u03bf\u03c0\u03cc\u03c4\u03b5 \u03b4\u03b5\u03bd \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03c0\u03bf\u03c3\u03bf\u03c4\u03b9\u03ba\u03cc metric \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03c5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u0395\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd, \u03ba\u03ac\u03b8\u03b5 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1 \u03b5\u03be\u03b5\u03c4\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1\u03bd \u03b5\u03b9\u03b4\u03b9\u03ba\u03cc \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 inter-annotator agreement. \u03a4\u03b1 heatmaps \u03bc\u03b5\u03b9\u03ce\u03bd\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b1\u03bd\u03b7\u03c3\u03c5\u03c7\u03af\u03b1 \u03cc\u03c4\u03b9 \u03ba\u03c5\u03c1\u03b9\u03b1\u03c1\u03c7\u03b5\u03af \u03b5\u03bc\u03c6\u03b1\u03bd\u03ce\u03c2 \u03c4\u03bf \u03c6\u03cc\u03bd\u03c4\u03bf, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03bf\u03c5\u03bd \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ac \u03bf\u03c1\u03b8\u03ae \u03b1\u03b9\u03c4\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7.<\/p>\n<aside class=\"td-article-note\"><strong>\u03a4\u03b1 heatmaps \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03b5\u03be\u03ae\u03b3\u03b7\u03c3\u03b7:<\/strong> \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03c0\u03bf\u03cd \u03c3\u03c5\u03b3\u03ba\u03b5\u03bd\u03c4\u03c1\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2, \u03cc\u03c7\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03c4\u03b1 \u03c3\u03c9\u03c3\u03c4\u03ac \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ac \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03bc\u03b5 \u03c4\u03bf\u03bd \u03c3\u03c9\u03c3\u03c4\u03cc \u03c4\u03c1\u03cc\u03c0\u03bf. \u0397 \u03b5\u03bc\u03c0\u03b9\u03c3\u03c4\u03bf\u03c3\u03cd\u03bd\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 error review, subgroup tests \u03ba\u03b1\u03b9 prospective evidence.<\/aside>\n<h2 id=\"stress-tests\">\u03a4\u03b1 stress tests \u03b1\u03c0\u03bf\u03ba\u03ac\u03bb\u03c5\u03c8\u03b1\u03bd \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf failure mode<\/h2>\n<p>\u03a3\u03c4\u03bf held-out test set \u03c0\u03c1\u03bf\u03c3\u03c4\u03ad\u03b8\u03b7\u03ba\u03b1\u03bd \u03c3\u03c5\u03bd\u03b8\u03b5\u03c4\u03b9\u03ba\u03ac Gaussian, stripe \u03ba\u03b1\u03b9 salt-and-pepper noise. \u03a7\u03c9\u03c1\u03af\u03c2 \u03b8\u03cc\u03c1\u03c5\u03b2\u03bf, \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b5\u03af\u03c7\u03b5 sensitivity 87,4% \u03ba\u03b1\u03b9 specificity 86,5%. \u039c\u03b5 Gaussian noise 20%, \u03b7 sensitivity \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03bd\u03b5 68,8%. \u039c\u03b5 stripe noise 7%, \u03ad\u03c0\u03b5\u03c3\u03b5 \u03c3\u03c4\u03bf 51,7%.<\/p>\n<p>\u03a4\u03bf \u03c0\u03b9\u03bf \u03b1\u03bd\u03b7\u03c3\u03c5\u03c7\u03b7\u03c4\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03c3\u03c4\u03bf salt-and-pepper noise. \u03a3\u03b5 \u03ad\u03bd\u03c4\u03b1\u03c3\u03b7 7%, \u03b7 sensitivity \u03ba\u03b1\u03c4\u03ad\u03c1\u03c1\u03b5\u03c5\u03c3\u03b5 \u03c3\u03c4\u03bf 5,1%, \u03b5\u03bd\u03ce \u03b7 specificity \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ac \u03c3\u03c4\u03bf 99,3%. \u03a4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03c1\u03bf\u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03cc\u03c4\u03b1\u03bd \u03c3\u03c4\u03b7 \u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b7 \u03ba\u03bb\u03ac\u03c3\u03b7 \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03bf\u03b9\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03bd \u03b1\u03ba\u03bc\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c5\u03c6\u03ad\u03c2.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03bb\u03b1\u03c3\u03b9\u03ba\u03cc dashboard trap: \u03ad\u03bd\u03b1\u03c2 \u03b4\u03b5\u03af\u03ba\u03c4\u03b7\u03c2 \u03bc\u03bf\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b5\u03bd\u03ce \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03b1\u03c0\u03bf\u03c4\u03c5\u03b3\u03c7\u03ac\u03bd\u03b5\u03b9 \u03c3\u03c4\u03bf\u03bd \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf \u03c3\u03c4\u03cc\u03c7\u03bf. \u0397 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03cd\u03b8\u03b7\u03c3\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 sensitivity, specificity, input-quality gates, \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03c9\u03bd, \u03c4\u03cd\u03c0\u03bf \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae\u03c2 \u03ba\u03b1\u03b9 drift. \u03a4\u03bf \u03af\u03b4\u03b9\u03bf failure mode \u03b4\u03b5\u03bd \u03b8\u03b1 \u03c6\u03b1\u03bd\u03b5\u03af \u03b1\u03bd \u03c4\u03bf dashboard \u03ba\u03c1\u03b1\u03c4\u03ac \u03bc\u03cc\u03bd\u03bf accuracy \u03ae throughput.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-kicker\">Go\/no-go \u03b3\u03b9\u03b1 edge AI triage<\/p>\n<p class=\"td-decision-title\">\u039a\u03b1\u03bc\u03af\u03b1 \u00ab\u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b7\u00bb \u03ad\u03be\u03bf\u03b4\u03bf\u03c2 \u03c7\u03c9\u03c1\u03af\u03c2 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b5\u03b9\u03c3\u03cc\u03b4\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae \u03c0\u03b1\u03c1\u03b1\u03c0\u03bf\u03bc\u03c0\u03ae<\/p>\n<p>Go \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 image-quality gate \u03c0\u03bf\u03c5 \u03ad\u03c7\u03b5\u03b9 \u03b5\u03c0\u03b9\u03ba\u03c5\u03c1\u03c9\u03b8\u03b5\u03af \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ac, logging \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae\u03c2 \u03ba\u03b1\u03b9 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7\u03c2, thresholds \u03b1\u03bd\u03ac \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c1\u03bf\u03ae, \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03cd\u03b8\u03b7\u03c3\u03b7 sensitivity \u03ba\u03b1\u03b9 subgroup performance, \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b5\u03c0\u03bf\u03c0\u03c4\u03b5\u03af\u03b1 \u03ba\u03b1\u03b9 fallback \u03cc\u03c4\u03b1\u03bd \u03b7 \u03b5\u03af\u03c3\u03bf\u03b4\u03bf\u03c2 \u03b2\u03b3\u03b1\u03af\u03bd\u03b5\u03b9 \u03ad\u03be\u03c9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae. No-go \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b1\u03c3\u03b1\u03c6\u03ae\u03c2 \u03bb\u03ae\u03c8\u03b7, \u03ac\u03b3\u03bd\u03c9\u03c3\u03c4\u03bf device profile, \u03be\u03b1\u03c6\u03bd\u03b9\u03ba\u03ae \u03bc\u03b5\u03c4\u03b1\u03c4\u03cc\u03c0\u03b9\u03c3\u03b7 \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03c9\u03bd \u03ae \u03c5\u03c8\u03b7\u03bb\u03ae specificity \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03bf\u03b4\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c0\u03c4\u03ce\u03c3\u03b7 \u03c4\u03b7\u03c2 sensitivity.<\/p>\n<\/div>\n<h2 id=\"on-device\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac on-device<\/h2>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 17 MB, peak \u03c7\u03c1\u03ae\u03c3\u03b7 1-2 CPU cores \u03b3\u03b9\u03b1 \u03bb\u03af\u03b3\u03b1 milliseconds \u03ba\u03b1\u03b9 inference \u03c3\u03b5 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf. \u0391\u03c5\u03c4\u03ac \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03bd\u03b8\u03b1\u03c1\u03c1\u03c5\u03bd\u03c4\u03b9\u03ba\u03ac engineering claims, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf paper \u03b4\u03b5\u03bd \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03b5\u03b9 benchmark \u03b1\u03bd\u03ac \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc smartphone, \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1\u03c2, \u03b8\u03b5\u03c1\u03bc\u03b9\u03ba\u03ae \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03ae prospective \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03c3\u03c4\u03bf \u03c0\u03b5\u03b4\u03af\u03bf.<\/p>\n<p>\u03a4\u03bf \u03bc\u03b9\u03ba\u03c1\u03cc footprint \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc. Offline inference \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 latency, \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c3\u03c5\u03bd\u03b4\u03b5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b5\u03c5\u03b1\u03af\u03c3\u03b8\u03b7\u03c4\u03c9\u03bd \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd \u03c3\u03c4\u03bf cloud. \u038c\u03bc\u03c9\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf product pipeline: camera guidance, quality gate, \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae\u03c2 \u03b1\u03c0\u03bf\u03b8\u03ae\u03ba\u03b5\u03c5\u03c3\u03b7, consent, model versioning, crash recovery, \u03b1\u03bd\u03b1\u03c4\u03c1\u03bf\u03c6\u03bf\u03b4\u03cc\u03c4\u03b7\u03c3\u03b7 \u03c0\u03c1\u03bf\u03c2 \u03c4\u03bf\u03bd \u03b5\u03c0\u03b1\u03b3\u03b3\u03b5\u03bb\u03bc\u03b1\u03c4\u03af\u03b1 \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b1\u03b3\u03b5\u03b3\u03c1\u03b1\u03bc\u03bc\u03ad\u03bd\u03b7 \u03c0\u03b1\u03c1\u03b1\u03c0\u03bf\u03bc\u03c0\u03ae.<\/p>\n<p>\u0397 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03b5\u03bd\u03cc\u03c2 edge \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03ac\u03c4\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf \u03bd\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03cc \u03b4\u03af\u03ba\u03c4\u03c5\u03bf. \u0397 \u03c0\u03b5\u03c1\u03af\u03c0\u03c4\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 <a href=\"https:\/\/twodots.gr\/fedopal-mia-mono-epikoinonia-synergatiki-ai-edge\/\">FedOPAL \u03b3\u03b9\u03b1 \u03c3\u03c5\u03bd\u03b5\u03c1\u03b3\u03b1\u03c4\u03b9\u03ba\u03ae AI \u03c3\u03c4\u03bf edge<\/a> \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af compute, \u03b5\u03c0\u03b9\u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03b1\u03c6\u03bf\u03c1\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<h2 id=\"governance\">\u0391\u03c0\u03cc \u03c4\u03bf paper \u03c3\u03b5 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 product governance<\/h2>\n<p>\u03a0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 patient-level splits, \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03ba\u03cd\u03c1\u03c9\u03c3\u03b7 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03bf\u03cd\u03c2 \u03c0\u03bb\u03b7\u03b8\u03c5\u03c3\u03bc\u03bf\u03cd\u03c2 \u03ba\u03b1\u03b9 smartphones, \u03c0\u03bf\u03bb\u03bb\u03bf\u03af annotators, \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c3\u03c5\u03bc\u03c6\u03c9\u03bd\u03af\u03b1\u03c2, \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 thresholds \u03ba\u03b1\u03b9 prospective \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03c4\u03b7 \u03c1\u03bf\u03ae \u03c6\u03c1\u03bf\u03bd\u03c4\u03af\u03b4\u03b1\u03c2. \u0397 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03b1\u03bb\u03cd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03bd\u03ac \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ae, \u03b3\u03b5\u03c9\u03b3\u03c1\u03b1\u03c6\u03af\u03b1, \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bb\u03ae\u03c8\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c5\u03c0\u03bf\u03bf\u03bc\u03ac\u03b4\u03b1, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c9\u03c2 pooled average.<\/p>\n<p>\u03a4\u03bf FDA Good Machine Learning Practice \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1 \u03c9\u03c2 \u03b8\u03ad\u03bc\u03b1 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03bf\u03cd \u03ba\u03cd\u03ba\u03bb\u03bf\u03c5 \u03b6\u03c9\u03ae\u03c2. \u03a4\u03bf NIST AI RMF \u03b6\u03b7\u03c4\u03ac \u03bd\u03b1 \u03b5\u03bd\u03c3\u03c9\u03bc\u03b1\u03c4\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bf\u03b9 \u03c0\u03b1\u03c1\u03ac\u03b3\u03bf\u03bd\u03c4\u03b5\u03c2 \u03b1\u03be\u03b9\u03bf\u03c0\u03b9\u03c3\u03c4\u03af\u03b1\u03c2 \u03c3\u03c4\u03bf\u03bd \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc, \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03c0\u03c4\u03c5\u03be\u03b7, \u03c4\u03b7 \u03c7\u03c1\u03ae\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7. \u0393\u03b9\u03b1 \u03c4\u03b7\u03bd \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2 \u03b1\u03c5\u03c4\u03cc \u03bc\u03b5\u03c4\u03b1\u03c6\u03c1\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 traceable requirements, risk register, data governance, monitoring, change control \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b5\u03c5\u03b8\u03cd\u03bd\u03b7.<\/p>\n<p>\u0397 \u03b4\u03b9\u03b1\u03c6\u03ac\u03bd\u03b5\u03b9\u03b1 \u03b4\u03b5\u03bd \u03b5\u03be\u03b1\u03bd\u03c4\u03bb\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ad\u03bd\u03b1 score \u03ae \u03ad\u03bd\u03b1 heatmap. \u03a4\u03b1 <a href=\"https:\/\/twodots.gr\/model-cards-open-weight-ai-governance\/\">model cards \u03ba\u03b1\u03b9 \u03b7 \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7 AI<\/a> \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 \u03cc\u03c4\u03b1\u03bd \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c6\u03bf\u03c5\u03bd \u03c4\u03bf\u03bd intended use, \u03c4\u03bf\u03c5\u03c2 \u03b1\u03c0\u03b1\u03b3\u03bf\u03c1\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf\u03c5\u03c2 \u03b9\u03c3\u03c7\u03c5\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2, \u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03c4\u03b1 thresholds, \u03c4\u03b1 failure modes \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c0\u03cc\u03c4\u03b5 \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03b4\u03ce\u03c3\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c3\u03b5 \u03ac\u03bd\u03b8\u03c1\u03c9\u03c0\u03bf.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u0395\u03c0\u03c4\u03ac \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03b9 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc pilot \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c0\u03b5\u03b4\u03af\u03bf<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">Step 1<\/span><strong>\u039a\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf\u03bd \u03b1\u03ba\u03c1\u03b9\u03b2\u03ae intended use<\/strong>\n<p>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03b1\u03bd \u03c4\u03bf \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 image triage, referral prioritisation \u03ae \u03ba\u03ac\u03c4\u03b9 \u03ac\u03bb\u03bb\u03bf. \u0391\u03c0\u03b1\u03b3\u03bf\u03c1\u03b5\u03cd\u03c3\u03c4\u03b5 \u03c1\u03b7\u03c4\u03ac claims \u03b4\u03b9\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7\u03c2 \u03ae \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7\u03c2 \u03b5\u03b9\u03b4\u03b9\u03ba\u03bf\u03cd \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03ad\u03c7\u03bf\u03c5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b7\u03b8\u03b5\u03af.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 2<\/span><strong>\u03a7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03c3\u03b5 \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03bf \u03b1\u03c3\u03b8\u03b5\u03bd\u03bf\u03cd\u03c2<\/strong>\n<p>\u0391\u03c0\u03bf\u03c4\u03c1\u03ad\u03c8\u03c4\u03b5 leakage \u03bc\u03b5\u03c4\u03b1\u03be\u03cd training, validation \u03ba\u03b1\u03b9 test \u03ba\u03b1\u03b9 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03bf \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03cc \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03b1\u03c0\u03cc \u03ac\u03bb\u03bb\u03bf\u03bd \u03c0\u03bb\u03b7\u03b8\u03c5\u03c3\u03bc\u03cc \u03ba\u03b1\u03b9 \u03b4\u03af\u03ba\u03c4\u03c5\u03bf \u03c3\u03c5\u03bb\u03bb\u03bf\u03b3\u03ae\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 3<\/span><strong>\u0395\u03c0\u03b9\u03ba\u03c5\u03c1\u03ce\u03c3\u03c4\u03b5 \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bb\u03ae\u03c8\u03b7\u03c2<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 blur, \u03c6\u03c9\u03c4\u03b9\u03c3\u03bc\u03cc, \u03b1\u03c0\u03cc\u03c3\u03c4\u03b1\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bf\u03c1\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b5 \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03b1 \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03b4\u03ad\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1, \u03cc\u03c7\u03b9 \u03bc\u03b5 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03b1\u03b9\u03c3\u03b8\u03b7\u03c4\u03b9\u03ba\u03ae \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 4<\/span><strong>\u0394\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b5 \u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ad\u03c2 \u03c3\u03c4\u03cc\u03c7\u03bf\u03c5<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 latency, \u03bc\u03bd\u03ae\u03bc\u03b7, \u03b5\u03bd\u03ad\u03c1\u03b3\u03b5\u03b9\u03b1, \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1, crashes \u03ba\u03b1\u03b9 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03b1\u03bd\u03ac camera pipeline, \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03ba\u03b1\u03b9 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 5<\/span><strong>\u0392\u03b1\u03b8\u03bc\u03bf\u03bd\u03bf\u03bc\u03ae\u03c3\u03c4\u03b5 thresholds \u03c3\u03c4\u03b7 \u03c1\u03bf\u03ae \u03c0\u03b1\u03c1\u03b1\u03c0\u03bf\u03bc\u03c0\u03ae\u03c2<\/strong>\n<p>\u03a3\u03c5\u03bd\u03b4\u03ad\u03c3\u03c4\u03b5 sensitivity, specificity \u03ba\u03b1\u03b9 predictive values \u03bc\u03b5 prevalence, \u03c7\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03b9\u03b4\u03b9\u03ba\u03ce\u03bd \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c8\u03b5\u03c5\u03b4\u03ce\u03c2 \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ce\u03bd \u03ba\u03b1\u03b9 \u03b8\u03b5\u03c4\u03b9\u03ba\u03ce\u03bd.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 6<\/span><strong>\u03a0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03ae\u03c3\u03c4\u03b5 drift \u03ba\u03b1\u03b9 failure signatures<\/strong>\n<p>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 input quality, \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ad\u03c2, \u03c5\u03c0\u03bf\u03bf\u03bc\u03ac\u03b4\u03b5\u03c2, \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03bc\u03ae scores \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc\u03c4\u03bf\u03bc\u03b5\u03c2 \u03b1\u03c0\u03bf\u03ba\u03bb\u03af\u03c3\u03b5\u03b9\u03c2 \u03cc\u03c0\u03c9\u03c2 \u03b7 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ae \u03ac\u03bd\u03bf\u03b4\u03bf\u03c2 specificity \u03c5\u03c0\u03cc impulse noise.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 7<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 handoff, incident response \u03ba\u03b1\u03b9 rollback<\/strong>\n<p>\u0394\u03ce\u03c3\u03c4\u03b5 \u03c3\u03c4\u03bf\u03bd \u03b5\u03c0\u03b1\u03b3\u03b3\u03b5\u03bb\u03bc\u03b1\u03c4\u03af\u03b1 \u03c3\u03b1\u03c6\u03ae \u03bb\u03cc\u03b3\u03bf \u03c0\u03b1\u03c1\u03b1\u03c0\u03bf\u03bc\u03c0\u03ae\u03c2, \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc override \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03b4\u03c1\u03bf\u03bc\u03ae \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac\u03c2\u00b7 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03b1\u03cd\u03c3\u03b7\u03c2 \u03ae \u03b5\u03c0\u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac\u03c2 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7\u03c2.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u03a3\u03b5 \u03ba\u03ac\u03b8\u03b5 pilot, \u03c4\u03b1 clinical safety KPIs \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c4\u03c9\u03bd vanity metrics. Downloads, inference speed \u03ae adoption \u03b4\u03b5\u03bd \u03b1\u03c0\u03b1\u03bd\u03c4\u03bf\u03cd\u03bd \u03b1\u03bd \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c7\u03ac\u03bd\u03b5\u03b9 \u03b5\u03c0\u03b9\u03ba\u03af\u03bd\u03b4\u03c5\u03bd\u03b5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 audit trail, review \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03c9\u03bd \u03bb\u03b1\u03b8\u03ce\u03bd \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 go\/no-go \u03b1\u03c0\u03cc \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03bf\u03cd\u03c2, product, data protection \u03ba\u03b1\u03b9 engineering \u03bc\u03b1\u03b6\u03af.<\/p>\n<h2 id=\"symperasma\">\u03a4\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 AI \u03ba\u03b1\u03b9 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc \u03c5\u03b2\u03c1\u03b9\u03b4\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b2\u03ac\u03c3\u03b7 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03b2\u03b1\u03c1\u03cd\u03c4\u03b5\u03c1\u03bf pipeline \u03cc\u03c4\u03b1\u03bd \u03bf \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 edge deployment. \u03a4\u03bf MobileViTv2 \u03c3\u03c5\u03bd\u03b4\u03cd\u03b1\u03c3\u03b5 4,9 \u03b5\u03ba\u03b1\u03c4. \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2, \u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03ac augmentations \u03ba\u03b1\u03b9 weighted cross-entropy \u03bc\u03b5 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ae image-level \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7 \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf retrospective dataset.<\/p>\n<p>\u03a4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b1, \u03c4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c5\u03c3\u03b9\u03ce\u03b4\u03b7: \u03bc\u03af\u03b1 \u03c7\u03ce\u03c1\u03b1, \u03b1\u03b4\u03c5\u03bd\u03b1\u03bc\u03af\u03b1 patient-level split, \u03ad\u03bd\u03b1\u03c2 specialist \u03b1\u03bd\u03ac \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1, 244 segmentation masks, \u03c0\u03bf\u03b9\u03bf\u03c4\u03b9\u03ba\u03ae \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03c5\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1, \u03c3\u03c5\u03bd\u03b8\u03b5\u03c4\u03b9\u03ba\u03ac stress tests \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c5\u03c3\u03af\u03b1 \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae\u03c2 \u03ae prospective validation. \u03a4\u03bf 17 MB \u03b5\u03af\u03bd\u03b1\u03b9 \u03b8\u03b5\u03c4\u03b9\u03ba\u03cc deployment \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc, \u03cc\u03c7\u03b9 \u03c0\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03b7\u03c4\u03b9\u03ba\u03cc \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae\u03c2 \u03b5\u03c4\u03bf\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2.<\/p>\n<p>\u0393\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03b1\u03c0\u03c4\u03cd\u03c3\u03c3\u03b5\u03b9 AI, \u03c4\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03b5\u03b9 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae, \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, metrics \u03ba\u03b1\u03b9 workflow \u03c9\u03c2 \u03ad\u03bd\u03b1 \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1. \u03a4\u03bf \u03bc\u03b9\u03ba\u03c1\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03bf\u03b9 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf\u03b9 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2, \u03b7 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03c0\u03b1\u03c1\u03b1\u03c0\u03bf\u03bc\u03c0\u03ae, \u03b7 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03cd\u03b8\u03b7\u03c3\u03b7 drift \u03ba\u03b1\u03b9 \u03b7 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03b5\u03be\u03af\u03c3\u03bf\u03c5 \u03b1\u03c5\u03c3\u03c4\u03b7\u03c1\u03ac.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-kicker\">\u0391\u03c0\u03cc \u03c4\u03bf \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03cc metric \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf workflow<\/p>\n<p class=\"td-service-cta-title\">\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 AI \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03bc\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b5\u03c0\u03bf\u03c0\u03c4\u03b5\u03af\u03b1<\/p>\n<p>\u0397 TWO DOTS \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af intended use, \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, acceptance tests, monitoring, privacy, handoffs \u03ba\u03b1\u03b9 rollback \u03ce\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1 AI workflow \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03ba\u03b1\u03b9 \u03b5\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03bf \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf demo.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03a3\u03c5\u03b6\u03b7\u03c4\u03ae\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1\u03bd \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf AI \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03cc<\/a><\/div>\n<\/div>\n<\/section>\n<section id=\"sychnes-erotiseis\" class=\"td-faq-section\">\n<div class=\"td-faq\">\n<p class=\"td-faq-heading\">Frequently Asked Questions (FAQs)<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0394\u03b9\u03b1\u03b3\u03b9\u03b3\u03bd\u03ce\u03c3\u03ba\u03b5\u03b9 \u03c4\u03bf MobileViTv2 \u03ba\u03b1\u03c1\u03ba\u03af\u03bd\u03bf \u03c3\u03c4\u03cc\u03bc\u03b1\u03c4\u03bf\u03c2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af \u03b4\u03c5\u03b1\u03b4\u03b9\u03ba\u03cc image triage \u03c3\u03b5 \u00ab\u03cd\u03c0\u03bf\u03c0\u03c4\u03b5\u03c2\u00bb \u03ba\u03b1\u03b9 \u00ab\u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b5\u03c2\u00bb \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2, \u03bc\u03b5 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac \u03c3\u03b5 \u03c0\u03c1\u03bf\u03c3\u03c9\u03c1\u03b9\u03bd\u03ad\u03c2 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b5\u03c2. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af prospective \u03b4\u03b9\u03b1\u03b3\u03bd\u03c9\u03c3\u03c4\u03b9\u03ba\u03ae \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae \u03bf\u03cd\u03c4\u03b5 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03b5\u03b9\u03b4\u03b9\u03ba\u03bf\u03cd.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03cc\u03c3\u03b5\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039c\u03b5\u03c4\u03ac \u03c4\u03bf\u03bd \u03ba\u03b1\u03b8\u03b1\u03c1\u03b9\u03c3\u03bc\u03cc \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd 29.574 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2: 4.887 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b5\u03c2 \u03ba\u03b1\u03b9 24.687 \u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b5\u03c2. \u03a7\u03c9\u03c1\u03af\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03c3\u03b5 17.744 training, 5.915 validation \u03ba\u03b1\u03b9 5.915 test \u03b5\u03b9\u03ba\u03cc\u03bd\u03b5\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03b5\u03c0\u03b9\u03bb\u03ad\u03c7\u03b8\u03b7\u03ba\u03b5 \u03c4\u03bf MobileViTv2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf MobileViTv2-1.0, \u03bc\u03b5 4,9 \u03b5\u03ba\u03b1\u03c4. \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2, \u03ad\u03b4\u03c9\u03c3\u03b5 \u03c4\u03bf \u03c0\u03b9\u03bf \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03b9\u03c3\u03bf\u03b6\u03cd\u03b3\u03b9\u03bf sensitivity \u03ba\u03b1\u03b9 specificity. \u03a3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf\u03c0\u03b9\u03ba\u03ac convolutional \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03bc\u03b5 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03bf context \u03bc\u03ad\u03c3\u03c9 \u03b1\u03c0\u03bf\u03b4\u03bf\u03c4\u03b9\u03ba\u03ae\u03c2 attention.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 \u03ae\u03c4\u03b1\u03bd \u03b7 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03b7\u03bd \u03c0\u03b5\u03bd\u03c4\u03b1\u03c0\u03bb\u03ae cross-validation \u03b7 \u03bc\u03ad\u03c3\u03b7 sensitivity \u03ae\u03c4\u03b1\u03bd 83,2% \u00b11,5 \u03ba\u03b1\u03b9 \u03b7 specificity 86,0% \u00b10,8. \u03a4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf checkpoint \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 87,4% sensitivity, 86,5% specificity \u03ba\u03b1\u03b9 97,2% NPV \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf test set.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 NPV 97,2%;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf test set, \u03c4\u03bf 97,2% \u03c4\u03c9\u03bd \u03b1\u03c1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ce\u03bd \u03c0\u03c1\u03bf\u03b2\u03bb\u03ad\u03c8\u03b5\u03c9\u03bd \u03c4\u03bf\u03c5 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c5 checkpoint \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf\u03cd\u03c3\u03b5 \u03c3\u03b5 \u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b7 \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b1. \u0397 NPV \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf prevalence \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03cd\u03c3\u03b9\u03b1 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03bf\u03bd \u03c0\u03bb\u03b7\u03b8\u03c5\u03c3\u03bc\u03cc.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03b1\u03c6\u03b1\u03b9\u03c1\u03ad\u03b8\u03b7\u03ba\u03b5 \u03c4\u03bf Segment Anything Model;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03b1 zero-shot coarse masks \u03ad\u03ba\u03c1\u03c5\u03b2\u03b1\u03bd \u03c3\u03c5\u03c7\u03bd\u03ac \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03b7\u03c2 \u03c3\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u03c2 \u03ba\u03bf\u03b9\u03bb\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03ad\u03b4\u03c9\u03c3\u03b1\u03bd \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2. \u0397 \u03b1\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03b7 \u03c4\u03bf\u03c5 SAM \u03b1\u03c0\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03c4\u03bf training pipeline.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf failure mode \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 stress tests;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039c\u03b5 salt-and-pepper noise 7%, \u03b7 sensitivity \u03ba\u03b1\u03c4\u03ad\u03c1\u03c1\u03b5\u03c5\u03c3\u03b5 \u03c3\u03c4\u03bf 5,1%, \u03b5\u03bd\u03ce \u03b7 specificity \u03b1\u03bd\u03ad\u03b2\u03b7\u03ba\u03b5 \u03c4\u03b5\u03c7\u03bd\u03b7\u03c4\u03ac \u03c3\u03c4\u03bf 99,3%. \u03a4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03c1\u03bf\u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03cc\u03c4\u03b1\u03bd \u03c3\u03c4\u03b7 \u03bc\u03b7 \u03cd\u03c0\u03bf\u03c0\u03c4\u03b7 \u03ba\u03bb\u03ac\u03c3\u03b7 \u03cc\u03c4\u03b1\u03bd \u03b1\u03bb\u03bb\u03bf\u03b9\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03bd \u03b1\u03ba\u03bc\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c5\u03c6\u03ad\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03bb\u03b5\u03af\u03c0\u03b5\u03b9 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 patient-level splits, \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 prospective \u03b5\u03c0\u03b9\u03ba\u03cd\u03c1\u03c9\u03c3\u03b7, \u03c0\u03bf\u03bb\u03bb\u03bf\u03af annotators, \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03c3\u03ba\u03b5\u03c5\u03ad\u03c2, subgroup analysis, quality gates \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae\u03c2 \u03c1\u03bf\u03ae \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7\u03c2 \u03c0\u03b1\u03c1\u03b1\u03c0\u03bf\u03bc\u03c0\u03ae\u03c2.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">Sources<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2608.21583\" target=\"_blank\" rel=\"noopener\">arXiv \u2014 Robust Lightweight Deep Learning Models for Oral Cancer Screening<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/html\/2608.21583\" target=\"_blank\" rel=\"noopener\">arXiv HTML \u2014 \u03c0\u03bb\u03ae\u03c1\u03b7\u03c2 \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03af\u03b1, \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2, stress tests \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/OCS-Tanuh\/Conference-AIMLSystems2026\" target=\"_blank\" rel=\"noopener\">GitHub \u2014 \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03bf\u03c2 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1\u03c2 \u03c4\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2 \u03b3\u03b9\u03b1 MobileViTv2<\/a><\/li>\n<li><a href=\"https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/good-machine-learning-practice-medical-device-development-guiding-principles\" target=\"_blank\" rel=\"noopener\">FDA \u2014 Good Machine Learning Practice \u03b3\u03b9\u03b1 AI\/ML medical devices<\/a><\/li>\n<li><a href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" target=\"_blank\" rel=\"noopener\">NIST \u2014 AI Risk Management Framework<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 MobileViTv2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 AI screening \u03ba\u03b1\u03c1\u03ba\u03af\u03bd\u03bf\u03c5 \u03c3\u03c4\u03cc\u03bc\u03b1\u03c4\u03bf\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, quality gates \u03ba\u03b1\u03b9 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03ba\u03cd\u03c1\u03c9\u03c3\u03b7.<\/p>","protected":false},"author":1,"featured_media":98400,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[199],"tags":[18032,20620,7203,20619,20621],"class_list":["post-97809","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ai-stin-ygeia","tag-deep-learning","tag-edge-ai","tag-mobilevitv2","tag-iatriki-technologia"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":8}},"_links":{"self":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/97809","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/comments?post=97809"}],"version-history":[{"count":1,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/97809\/revisions"}],"predecessor-version":[{"id":98401,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/97809\/revisions\/98401"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media\/98400"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media?parent=97809"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/categories?post=97809"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/tags?post=97809"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}