{"id":"W4280577324","doi":"10.1016/j.compbiomed.2022.105581","title":"Knowledge distillation approach towards melanoma detection","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Residual neural network; Distillation; Inference; Artificial intelligence; Task (project management); Melanoma; Machine learning; Deep learning; Focus (optics); Melanoma diagnosis; Pattern recognition (psychology); Constraint (computer-aided design); Mathematics; Engineering; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001347327,0.0008088548,0.001000622,0.002505901,0.0007306823,0.001856751,0.001724751,0.001176901,0.007866816],"category_scores_gemma":[0.005662045,0.0002933306,0.001249477,0.001663025,0.0006862035,0.001934257,0.002426817,0.001797318,0.001973901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008957381,"about_ca_system_score_gemma":0.002347097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005875785,"about_ca_topic_score_gemma":0.006334001,"domain_scores_codex":[0.9988895,0.0003034744,0.00008021817,0.0002369754,0.0003924342,0.00009728603],"domain_scores_gemma":[0.9974058,0.001633885,0.00007906624,0.0002421798,0.0005661916,0.00007288426],"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":[0.0004471612,0.0004534778,0.001665701,0.0006369918,0.0001644359,0.0003238047,0.0002369969,0.0634184,0.007807203,0.03139004,0.01218153,0.8812743],"study_design_scores_gemma":[0.00005923749,0.000177534,0.000841632,0.0001324252,0.0001371432,0.0002189275,0.0001873723,0.8781896,0.01307108,0.08801179,0.01894379,0.00002944193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01683628,0.001232728,0.9688892,0.001668815,0.000165033,0.0002380038,0.001125043,0.002026379,0.007818467],"genre_scores_gemma":[0.3029323,0.001155725,0.6817384,0.0007682762,0.0002165932,0.0002638567,0.002816264,0.0001068152,0.01000181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007866816,"threshold_uncertainty_score":0.02631718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02279616338982202,"score_gpt":0.300754182731129,"score_spread":0.2779580193413069,"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."}}