{"id":"W3107839074","doi":"10.21203/rs.3.rs-493126/v1","title":"Cancer-Net SCa: Tailored Deep Neural Network Designs for Detection of Skin Cancer from Dermoscopy Images","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Skin cancer; Computer science; Deep learning; Cancer; Artificial neural network; Artificial intelligence; Machine learning; Process (computing); Suite; Audit; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007276498,0.0007586107,0.0002446519,0.0003803768,0.0001735165,0.0003858651,0.001110044,0.0007151197,0.002184427],"category_scores_gemma":[0.002383898,0.0002234596,0.0004458007,0.0001529661,0.0002921918,0.0005886214,0.0005302755,0.0008376066,0.0004790554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006996205,"about_ca_system_score_gemma":0.0006541047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003366509,"about_ca_topic_score_gemma":0.006236623,"domain_scores_codex":[0.9997812,0.00005240854,0.00001030308,0.00007839749,0.00005010309,0.00002769088],"domain_scores_gemma":[0.9995018,0.0001866759,0.00006366999,0.00005324659,0.0001582643,0.00003632952],"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":[0.0006439357,0.0003303225,0.008617765,0.0003419241,0.0001983616,0.0002957598,0.00009990163,0.7143204,0.03065968,0.004325528,0.01392512,0.2262413],"study_design_scores_gemma":[0.00002165272,0.0001785745,0.0006506022,0.00001063105,0.00001550684,0.00003970705,0.000008591618,0.9906229,0.00629438,0.001208444,0.0009409856,0.000007908542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4864904,0.001886289,0.4882733,0.001091762,0.0003291614,0.0004215503,0.001700829,0.01024831,0.009558458],"genre_scores_gemma":[0.8584141,0.00024286,0.134802,0.0003910621,0.00003831216,0.0002542856,0.001470966,0.0002238091,0.004162614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003366509,"threshold_uncertainty_score":0.007307589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07174625810642088,"score_gpt":0.3981326172293848,"score_spread":0.3263863591229639,"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."}}