{"id":"W2102427741","doi":"10.1109/tmi.2003.823062","title":"Automatic Identification of the Pectoral Muscle in Mammograms","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":211,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pectoral muscle; Pixel; Artificial intelligence; Hough transform; Computer vision; Computer science; Edge detection; Image processing; Mammography; Pattern recognition (psychology); Mathematics; Anatomy; Breast cancer; Image (mathematics); 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":[0.0003773419,0.00008031762,0.00009621738,0.0001312386,0.00009758578,0.00003962636,0.0005830351,0.00004200603,0.00005326544],"category_scores_gemma":[0.00002962219,0.00006499196,0.00007620491,0.000746362,0.0001240333,0.0003112795,0.000003879217,0.0003044127,0.0000160211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001964262,"about_ca_system_score_gemma":0.0001690458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002359911,"about_ca_topic_score_gemma":0.0001631372,"domain_scores_codex":[0.9986174,0.00006732493,0.0003316733,0.0002212148,0.0005958127,0.0001665964],"domain_scores_gemma":[0.9993293,0.00005703067,0.0000862597,0.0004168595,0.00003287988,0.0000777278],"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":[0.000002296945,0.0002167747,0.0001202339,0.00003121932,0.000006679015,0.000007705949,0.0009430322,0.01067165,0.005340059,0.0003592036,0.0000166645,0.9822845],"study_design_scores_gemma":[0.001285245,0.00003855928,0.01838479,0.0003903335,0.00001818728,0.00006146481,0.0001627298,0.8438486,0.129239,0.006205432,0.0001302958,0.0002353843],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08968316,0.00001964681,0.9047437,0.003977597,0.00129536,0.0001269394,8.350264e-7,0.0001077008,0.0000451145],"genre_scores_gemma":[0.9976102,0.000007844272,0.002060901,0.0002506068,0.00002162935,0.00002927423,9.994226e-8,0.000007103804,0.00001233621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9820491,"threshold_uncertainty_score":0.2650295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009787980422182364,"score_gpt":0.2593393645909012,"score_spread":0.2495513841687189,"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."}}