{"id":"W4381135038","doi":"10.32920/22734344.v1","title":"An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks","year":2023,"lang":"en","type":"preprint","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hyperparameter; Computer science; Artificial neural network; Metric (unit); Transfer of learning; Machine learning; Artificial intelligence; Bayesian network; Feature (linguistics); Coverage probability; Confidence interval; Pattern recognition (psychology); Data mining; Statistics; Mathematics","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":[0.009561625,0.002073847,0.001790056,0.003471566,0.0006597762,0.002573295,0.003077967,0.003067782,0.002102885],"category_scores_gemma":[0.05834346,0.0005702258,0.0009801558,0.001808076,0.00129417,0.003913812,0.002810991,0.00237778,0.0006924773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00275351,"about_ca_system_score_gemma":0.001607203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005741314,"about_ca_topic_score_gemma":0.005472858,"domain_scores_codex":[0.9896138,0.003565345,0.00080546,0.001515899,0.004081072,0.0004183388],"domain_scores_gemma":[0.9748757,0.01652136,0.001966494,0.00177792,0.004236745,0.0006217799],"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":[0.001375494,0.0003630308,0.03128904,0.0008600399,0.0005853351,0.0002528743,0.0001875579,0.5102554,0.00897367,0.01599215,0.01419262,0.4156729],"study_design_scores_gemma":[0.00003370213,0.0001415623,0.002455171,0.00007575288,0.0000571179,0.0001650348,0.00002781838,0.9828185,0.004574466,0.008232119,0.001386374,0.00003239656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07661942,0.004883586,0.9042386,0.001539716,0.0001691002,0.0003478428,0.002463935,0.0036144,0.006123324],"genre_scores_gemma":[0.7313471,0.001335055,0.2576059,0.0006873111,0.0002582668,0.0005277019,0.005707663,0.000579524,0.001951487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009561625,"threshold_uncertainty_score":0.05056733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065421883310438,"score_gpt":0.3768324511883592,"score_spread":0.2702902628573154,"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."}}