{"id":"W3202780099","doi":"10.30683/1929-2279.2019.08.03","title":"A Detail Process for CAD Based Breast Cancer Detection","year":2019,"lang":"en","type":"article","venue":"Journal of cancer research updates","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Breast cancer; CAD; Medicine; Disease; Biopsy; Cancer; Mammography; Yield (engineering); Radiology; Computer science; Oncology; Internal medicine; Engineering drawing; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001844874,0.0001480918,0.0002744986,0.0005335512,0.0002031823,0.0002437364,0.001100415,0.00009737627,0.0001768756],"category_scores_gemma":[0.0000822216,0.0001211285,0.0001445375,0.001023944,0.00007577954,0.001288646,0.00008479689,0.0006134144,0.0000173459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008332283,"about_ca_system_score_gemma":0.001545854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003655276,"about_ca_topic_score_gemma":0.0005655045,"domain_scores_codex":[0.9972808,0.000154356,0.0004264282,0.0003346904,0.001254573,0.0005491815],"domain_scores_gemma":[0.9963052,0.0002557853,0.0003740678,0.0003470088,0.002530251,0.0001877026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003099373,0.0002842964,0.03168491,0.001115842,0.0004143197,0.00002499317,0.0009345532,0.02931869,0.159342,0.0004806879,0.007106202,0.7661941],"study_design_scores_gemma":[0.004691551,0.001861822,0.01639272,0.000898735,0.00005571825,0.0001823574,0.0002628648,0.1987943,0.7203451,0.006516236,0.04938673,0.0006118991],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7667781,0.003988518,0.2065486,0.01715718,0.003664939,0.001441732,0.00007793823,0.0001038131,0.0002392103],"genre_scores_gemma":[0.9972501,0.0003007128,0.001133037,0.0001981364,0.0006956966,0.0002068254,4.164493e-7,0.00002617056,0.0001888865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7655822,"threshold_uncertainty_score":0.4939476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04864816383186417,"score_gpt":0.4113004547366876,"score_spread":0.3626522909048235,"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."}}