{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003036277,0.0013447,0.0006395278,0.0009830404,0.0004585854,0.0007470076,0.001329146,0.001203616,0.002503187],"category_scores_gemma":[0.004490541,0.0004495103,0.0009152762,0.0005398878,0.0005181442,0.000648887,0.0009155431,0.001442996,0.0009783314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001800788,"about_ca_system_score_gemma":0.001596848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02150141,"about_ca_topic_score_gemma":0.01537905,"domain_scores_codex":[0.9991781,0.0001593069,0.00006902804,0.0002384982,0.0002059184,0.0001490272],"domain_scores_gemma":[0.997357,0.0007573773,0.0002022021,0.0004003136,0.001184159,0.00009884615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007659365,0.001206516,0.02384397,0.0002965788,0.000264473,0.0002008971,0.0001292446,0.5905571,0.02086861,0.0009628183,0.004799924,0.356104],"study_design_scores_gemma":[0.00001595598,0.0001202084,0.001864677,0.00001419699,0.00002297616,0.00002735638,0.00001260456,0.9853537,0.01193477,0.0002741989,0.0003511469,0.000008063936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7183841,0.00163784,0.2636053,0.0004168212,0.0002760692,0.001116862,0.001719438,0.008075553,0.004768021],"genre_scores_gemma":[0.9042419,0.0001942151,0.0898791,0.0001327304,0.0000216299,0.0003698517,0.00232849,0.0001278144,0.002704186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02150141,"threshold_uncertainty_score":0.04275256,"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."}}