{"id":"W4388629053","doi":"10.3390/jimaging9110247","title":"Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method","year":2023,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Weighting; Artificial intelligence; Deep learning; Mammography; Key (lock); CAD; Breast cancer; Annotation; Pixel; Machine learning; Pattern recognition (psychology); Data mining; Computer vision; Cancer; 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":[0.001235729,0.0007087299,0.0007495165,0.0008686685,0.0003532818,0.0005477982,0.001386647,0.0008613183,0.001120705],"category_scores_gemma":[0.00235479,0.0004011547,0.000559448,0.000654701,0.0002052549,0.00111368,0.001182708,0.0008723065,0.0004530904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007180247,"about_ca_system_score_gemma":0.0008922601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008959696,"about_ca_topic_score_gemma":0.01159815,"domain_scores_codex":[0.9995673,0.00006469867,0.00002365727,0.0001459941,0.0001379346,0.00006026963],"domain_scores_gemma":[0.9993265,0.0001344938,0.00006216881,0.00008937577,0.0003426362,0.00004483394],"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":[0.0002205346,0.0001496083,0.004193183,0.0000580985,0.0001698327,0.0001181714,0.00008973962,0.3716121,0.01947419,0.003155275,0.00430605,0.5964532],"study_design_scores_gemma":[0.000003374645,0.00001907435,0.0002217181,0.000002329996,0.00001146996,0.00001434698,0.000004340181,0.9969366,0.001796578,0.0006378352,0.0003484704,0.000003964206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06836073,0.0004033502,0.9270123,0.0002130507,0.00007274898,0.00006146853,0.0001260461,0.001780534,0.001969744],"genre_scores_gemma":[0.7392176,0.0002421952,0.2533003,0.0002652807,0.00008616042,0.0001282525,0.0005154281,0.0001369731,0.006107682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008959696,"threshold_uncertainty_score":0.01781511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01912822891537277,"score_gpt":0.295623161842811,"score_spread":0.2764949329274382,"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."}}