{"id":"W3012937626","doi":"10.2196/18438","title":"Skin Lesion Classification With Deep Convolutional Neural Network: Process Development and Validation","year":2020,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Skin cancer; Deep learning; Artificial intelligence; Computer science; Stage (stratigraphy); Artificial neural network; Cancer; Skin lesion; Process (computing); Lesion; Pattern recognition (psychology); Machine learning; Dermatology; Medicine; Pathology; Internal medicine; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002743972,0.00009321838,0.0001482817,0.00004191627,0.00009624808,0.00001287075,0.00002980651,0.00006237318,0.00004638798],"category_scores_gemma":[0.000008876804,0.00007794874,0.0000131552,0.0001291671,0.00004681514,0.00004701909,0.00002139423,0.00009469055,0.00003154143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002848078,"about_ca_system_score_gemma":0.00004670072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.692662e-7,"about_ca_topic_score_gemma":0.000006621772,"domain_scores_codex":[0.9993032,0.00003014408,0.000175886,0.0002109619,0.0001315623,0.0001483128],"domain_scores_gemma":[0.9996811,0.00001633498,0.0000744853,0.0000655172,0.00006038128,0.0001022522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.009802572,0.00115312,0.5658134,0.004624992,0.000907243,0.002439313,0.01726415,0.00414628,0.009422041,0.02081158,0.03154958,0.3320657],"study_design_scores_gemma":[0.007012293,0.0008238922,0.5601283,0.0001280921,0.0002120575,0.01470528,0.002348397,0.195072,0.0119549,0.0001791451,0.2067042,0.0007313726],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827604,0.0000735348,0.007301683,0.008174449,0.00006202743,0.00055176,3.377689e-7,0.0001081405,0.000967693],"genre_scores_gemma":[0.9970697,0.000006207883,0.001077263,0.001530709,0.0000805808,0.00009058396,0.00005695257,0.00001035865,0.00007762197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3313343,"threshold_uncertainty_score":0.3178657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03517673269553142,"score_gpt":0.2803862478007585,"score_spread":0.2452095151052271,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}