{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004951035,0.0004824019,0.0004371114,0.002681174,0.0001889491,0.0005221691,0.000502072,0.000398065,0.001040508],"category_scores_gemma":[0.001965393,0.0002839399,0.0002656902,0.0008708411,0.0001656521,0.0003965066,0.0004991819,0.0001888025,0.0008082939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001218065,"about_ca_system_score_gemma":0.0002059214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001179901,"about_ca_topic_score_gemma":0.002021905,"domain_scores_codex":[0.9995566,0.00006998408,0.00004210576,0.00009719904,0.0001859794,0.00004815999],"domain_scores_gemma":[0.9993461,0.0002526917,0.0001061394,0.00006279504,0.0001985179,0.00003377429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007471634,0.00009621846,0.02274412,0.0003565039,0.0000559013,0.0008044864,0.0001707147,0.00232771,0.5142714,0.0003105493,0.002347764,0.4557674],"study_design_scores_gemma":[0.0000862204,0.0004090422,0.4519461,0.0001245903,0.0001920857,0.007598584,0.0004355623,0.2431355,0.2869828,0.00075245,0.008240567,0.00009656126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7102234,0.001669182,0.2780636,0.0001167001,0.00007997327,0.0003853306,0.001148926,0.005070519,0.003242266],"genre_scores_gemma":[0.7303957,0.0008235661,0.2650602,0.00006437529,0.00005184315,0.0001085692,0.001524656,0.0001815542,0.001789577],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002681174,"threshold_uncertainty_score":0.003480852,"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."}}