{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002914335,0.0000958488,0.0002285825,0.0002617398,0.0001326202,0.000002009839,0.00004517709,0.00004897113,0.00007967249],"category_scores_gemma":[0.0000344548,0.00007844421,0.0000205341,0.0002815651,0.0001167524,0.00001177039,0.0001123333,0.0002061517,0.000002148267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001118701,"about_ca_system_score_gemma":0.00001846982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000051699,"about_ca_topic_score_gemma":0.00001254056,"domain_scores_codex":[0.9992705,0.00009850826,0.0001818677,0.0002493957,0.00006412498,0.0001356041],"domain_scores_gemma":[0.9997081,0.00004162975,0.00004409701,0.0001210357,0.00002148491,0.00006365428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007367822,0.0003323648,0.0167778,0.0001618573,0.0001063724,0.00009149635,0.002165059,0.00006354675,0.006140155,0.00600433,0.002341081,0.9650791],"study_design_scores_gemma":[0.0126046,0.008888617,0.3033329,0.0001196429,0.0002433339,0.005221352,0.003155345,0.07614113,0.000439835,0.003790335,0.5854766,0.00058624],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8971685,0.002572274,0.05167129,0.002600092,0.003200331,0.0008574708,0.00000356659,0.000168188,0.04175828],"genre_scores_gemma":[0.9981354,0.0000818419,0.0005413393,0.0004592536,0.0002395392,0.00004114279,0.0000506048,0.000006893054,0.0004440212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9644929,"threshold_uncertainty_score":0.3198862,"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."}}