{"id":"W3015673273","doi":"10.36227/techrxiv.12089514.v1","title":"COMPUTER VISION FOR SKIN CANCER DETECTION AND DIAGNOSIS","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Lakehead University","keywords":"Upload; Computer science; Artificial intelligence; Thresholding; Convolutional neural network; Similarity (geometry); Image (mathematics); Computer vision; Pattern recognition (psychology); Convolution (computer science); Inpainting; Artificial neural network; World Wide Web","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":[0.000618378,0.0006236334,0.0006052704,0.0009905295,0.0002601929,0.00141419,0.0005934787,0.001316641,0.007783312],"category_scores_gemma":[0.001816411,0.0002129937,0.0005460634,0.001218688,0.0004445151,0.0008709229,0.0007139814,0.00149291,0.005572723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000743236,"about_ca_system_score_gemma":0.0007088634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003515868,"about_ca_topic_score_gemma":0.002286569,"domain_scores_codex":[0.9992678,0.0001382629,0.00003058203,0.0001587552,0.0003464868,0.00005807292],"domain_scores_gemma":[0.9995189,0.0001443199,0.00003778227,0.00008280637,0.0001906008,0.0000256218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001188457,0.0001192501,0.001172342,0.0006274784,0.0000831562,0.0001282802,0.00005188658,0.0206806,0.01312235,0.02388434,0.07191954,0.868092],"study_design_scores_gemma":[0.00004843699,0.0002141214,0.008550416,0.0003469923,0.00007966322,0.001119826,0.0001659085,0.555083,0.02381011,0.1054084,0.3050906,0.00008265002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02073985,0.08236256,0.8369986,0.006189192,0.002471326,0.0002912762,0.002120752,0.005019771,0.04380673],"genre_scores_gemma":[0.5222629,0.06500531,0.3450163,0.001794878,0.002065161,0.0003770253,0.007299086,0.0004869179,0.0556924],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007783312,"threshold_uncertainty_score":0.02603781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331144828363373,"score_gpt":0.3013966602435577,"score_spread":0.2780852119599239,"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."}}