{"id":3371,"date":"2021-11-25T05:52:27","date_gmt":"2021-11-25T05:52:27","guid":{"rendered":"https:\/\/www.oferreiter.com\/?p=3371"},"modified":"2023-10-26T08:55:14","modified_gmt":"2023-10-26T08:55:14","slug":"a-patient-centric-dataset-of-images-and-metadata-for-identifying-melanomas-using-clinical-context","status":"publish","type":"post","link":"https:\/\/www.oferreiter.com\/en\/a-patient-centric-dataset-of-images-and-metadata-for-identifying-melanomas-using-clinical-context\/","title":{"rendered":"A patient-centric dataset of images and metadata for identifying melanomas using clinical context"},"content":{"rendered":"

Veronica Rotemberg<\/span><\/a>\u00a0<\/span>#<\/span><\/a>\u00a0<\/span>1<\/span><\/a>,\u00a0<\/span>Nicholas Kurtansky<\/span><\/a>\u00a0<\/span>#<\/span><\/a>\u00a0<\/span>2<\/span><\/a>,\u00a0<\/span>Brigid Betz-Stablein<\/span><\/a>\u00a0<\/span>3<\/span><\/a>,\u00a0<\/span>Liam Caffery<\/span><\/a>\u00a0<\/span>3<\/span><\/a>,\u00a0<\/span>Emmanouil Chousakos<\/span><\/a>\u00a0<\/span>2<\/span><\/a>\u00a0<\/span>4<\/span><\/a>,\u00a0<\/span>Noel Codella<\/span><\/a>\u00a0<\/span>5<\/span><\/a>,\u00a0<\/span>Marc Combalia<\/span><\/a>\u00a0<\/span>6<\/span><\/a>,\u00a0<\/span>Stephen Dusza<\/span><\/a>\u00a0<\/span>2<\/span><\/a>,\u00a0<\/span>Pascale Guitera<\/span><\/a>\u00a0<\/span>7<\/span><\/a>,\u00a0<\/span>David Gutman<\/span><\/a>\u00a0<\/span>8<\/span><\/a>,\u00a0<\/span>Allan Halpern<\/span><\/a>\u00a0<\/span>2<\/span><\/a>,\u00a0<\/span>Brian Helba<\/span><\/a>\u00a0<\/span>9<\/span><\/a>,\u00a0<\/span>Harald Kittler<\/span><\/a>\u00a0<\/span>10<\/span><\/a>,\u00a0<\/span>Kivanc Kose<\/span><\/a>\u00a0<\/span>2<\/span><\/a>,\u00a0<\/span>Steve Langer<\/span><\/a>\u00a0<\/span>11<\/span><\/a>,\u00a0<\/span>Konstantinos Lioprys<\/span><\/a>\u00a0<\/span>4<\/span><\/a>,\u00a0<\/span>Josep Malvehy<\/span><\/a>\u00a0<\/span>6<\/span><\/a>,\u00a0<\/span>Shenara Musthaq<\/span><\/a>\u00a0<\/span>2<\/span><\/a>\u00a0<\/span>12<\/span><\/a>,\u00a0<\/span>Jabpani Nanda<\/span><\/a>\u00a0<\/span>2<\/span><\/a>\u00a0<\/span>13<\/span><\/a>,\u00a0<\/span>Ofer Reiter<\/span><\/a>\u00a0<\/span>2<\/span><\/a>\u00a0<\/span>14<\/span><\/a>,\u00a0<\/span>George Shih<\/span><\/a>\u00a0<\/span>15<\/span><\/a>,\u00a0<\/span>Alexander Stratigos<\/span><\/a>\u00a0<\/span>4<\/span><\/a>,\u00a0<\/span>Philipp Tschandl<\/span><\/a>\u00a0<\/span>10<\/span><\/a>,\u00a0<\/span>Jochen Weber<\/span><\/a>\u00a0<\/span>2<\/span><\/a>,\u00a0<\/span>H Peter Soyer<\/span><\/a>\u00a0<\/span>3<\/span><\/a><\/p>\n

Abstract<\/span><\/h2>\n

Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients (20.8% with at least one melanoma, 79.2% with zero melanomas) from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 (1.8%) histopathologically confirmed melanomas compared with benign melanoma mimickers.<\/span><\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

Veronica Rotemberg\u00a0#\u00a01,\u00a0Nicholas Kurtansky\u00a0#\u00a02,\u00a0Brigid Betz-Stablein\u00a03,\u00a0Liam Caffery\u00a03,\u00a0Emmanouil Chousakos\u00a02\u00a04,\u00a0Noel Codella\u00a05,\u00a0Marc Combalia\u00a06,\u00a0Stephen Dusza\u00a02,\u00a0Pascale Guitera\u00a07,\u00a0David Gutman\u00a08,\u00a0Allan Halpern\u00a02,\u00a0Brian Helba\u00a09,\u00a0Harald Kittler\u00a010,\u00a0Kivanc Kose\u00a02,\u00a0Steve Langer\u00a011,\u00a0Konstantinos Lioprys\u00a04,\u00a0Josep Malvehy\u00a06,\u00a0Shenara Musthaq\u00a02\u00a012,\u00a0Jabpani Nanda\u00a02\u00a013,\u00a0Ofer Reiter\u00a02\u00a014,\u00a0George Shih\u00a015,\u00a0Alexander Stratigos\u00a04,\u00a0Philipp Tschandl\u00a010,\u00a0Jochen Weber\u00a02,\u00a0H Peter Soyer\u00a03 Abstract Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance […]<\/p>\n","protected":false},"author":1,"featured_media":2709,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3371","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-published-research"],"yoast_head":"\nA patient-centric dataset of images and metadata for identifying melanomas using clinical context - \u05d3"\u05e8 \u05e2\u05d5\u05e4\u05e8 \u05e8\u05d9\u05d9\u05d8\u05e8 \u05d0\u05d2\u05e8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.oferreiter.com\/en\/a-patient-centric-dataset-of-images-and-metadata-for-identifying-melanomas-using-clinical-context\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A patient-centric dataset of images and metadata for identifying melanomas using clinical context - \u05d3"\u05e8 \u05e2\u05d5\u05e4\u05e8 \u05e8\u05d9\u05d9\u05d8\u05e8 \u05d0\u05d2\u05e8\" \/>\n<meta property=\"og:description\" content=\"Veronica Rotemberg\u00a0#\u00a01,\u00a0Nicholas Kurtansky\u00a0#\u00a02,\u00a0Brigid Betz-Stablein\u00a03,\u00a0Liam Caffery\u00a03,\u00a0Emmanouil Chousakos\u00a02\u00a04,\u00a0Noel Codella\u00a05,\u00a0Marc Combalia\u00a06,\u00a0Stephen Dusza\u00a02,\u00a0Pascale Guitera\u00a07,\u00a0David Gutman\u00a08,\u00a0Allan Halpern\u00a02,\u00a0Brian Helba\u00a09,\u00a0Harald Kittler\u00a010,\u00a0Kivanc Kose\u00a02,\u00a0Steve Langer\u00a011,\u00a0Konstantinos Lioprys\u00a04,\u00a0Josep Malvehy\u00a06,\u00a0Shenara Musthaq\u00a02\u00a012,\u00a0Jabpani Nanda\u00a02\u00a013,\u00a0Ofer Reiter\u00a02\u00a014,\u00a0George Shih\u00a015,\u00a0Alexander Stratigos\u00a04,\u00a0Philipp Tschandl\u00a010,\u00a0Jochen Weber\u00a02,\u00a0H Peter Soyer\u00a03 Abstract Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. 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