{"id":"W4391069663","doi":"10.1067/j.cpradiol.2024.01.007","title":"Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspective","year":2024,"lang":"en","type":"article","venue":"Current Problems in Diagnostic Radiology","topic":"AI in cancer detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Medicine; Mammography; Breast cancer; Usability; Machine learning; Artificial intelligence; Deep learning; Medical physics; Digital mammography; Cancer; Computer science; Human–computer interaction; Internal 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":[0.0007094233,0.0002200683,0.0003023561,0.0005856921,0.00007355108,0.0003529679,0.00104214,0.0001218888,0.00000421322],"category_scores_gemma":[0.001679103,0.0002232967,0.00005958543,0.0009430895,0.0001314758,0.001307063,0.0003738024,0.0005235744,0.00002645255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006922584,"about_ca_system_score_gemma":0.0001534135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005453175,"about_ca_topic_score_gemma":0.00008753274,"domain_scores_codex":[0.9975499,0.0001547397,0.000576779,0.001034396,0.0001606441,0.0005235606],"domain_scores_gemma":[0.9951172,0.004046598,0.0001271708,0.0005500403,0.00009497417,0.00006397718],"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":[0.00001705739,0.0002733304,0.03669354,0.0005221142,0.000049726,0.00004057191,0.005254888,0.05387996,0.00001050566,0.1081363,0.001430398,0.7936916],"study_design_scores_gemma":[0.0005677182,0.0002528807,0.01089468,0.0003328964,0.00001057351,0.00009413128,0.0001832471,0.947579,0.000004190087,0.02448126,0.01532593,0.0002735404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008570828,0.01624269,0.9694501,0.0006486398,0.003650779,0.001017484,0.0000161369,0.0002914031,0.0001119545],"genre_scores_gemma":[0.9924788,0.001985876,0.004304665,0.00002124522,0.0004053453,0.0006085295,0.0001574551,0.00002311207,0.00001490361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9839081,"threshold_uncertainty_score":0.9105777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0522386207867534,"score_gpt":0.3179389957362764,"score_spread":0.265700374949523,"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."}}