{"id":"W2069716978","doi":"10.1118/1.4736530","title":"Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c‐means clustering and support vector machine segmentation","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":215,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; University of Pennsylvania; National Cancer Institute; National Institutes of Health; American Cancer Society; U.S. Department of Defense","keywords":"Mammography; Artificial intelligence; Computer science; Digital mammography; Cluster analysis; Breast cancer; Support vector machine; Thresholding; Pattern recognition (psychology); Segmentation; Computer vision; Medicine; Cancer; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006551213,0.0004714221,0.0004001594,0.002206188,0.0002939145,0.000637143,0.0008488299,0.0007636786,0.0006816682],"category_scores_gemma":[0.002195532,0.0002700646,0.0004580585,0.0008214129,0.0003529714,0.0004262179,0.000382732,0.0003319042,0.0003304689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006076168,"about_ca_system_score_gemma":0.001000201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005321607,"about_ca_topic_score_gemma":0.00491302,"domain_scores_codex":[0.9994491,0.00005186496,0.00004220575,0.0001182127,0.0002956994,0.00004291604],"domain_scores_gemma":[0.9992679,0.0001619387,0.0001114506,0.00006361491,0.0003714851,0.00002357758],"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":[0.0002806238,0.0001316141,0.008192049,0.0002106644,0.0000730142,0.000146196,0.0002128835,0.07090244,0.121349,0.001626295,0.00151023,0.7953649],"study_design_scores_gemma":[0.00001055464,0.00009617814,0.01204237,0.00002976228,0.00002738062,0.0003031344,0.00006244105,0.9354911,0.04937272,0.001043786,0.001473812,0.00004677573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09349018,0.0002269629,0.9037393,0.00008485748,0.00002357855,0.0001266142,0.0001125271,0.001351497,0.0008445586],"genre_scores_gemma":[0.3134899,0.0001654177,0.6849273,0.00004412877,0.00001652358,0.0001234822,0.0002208462,0.00007111751,0.0009413049],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005321607,"threshold_uncertainty_score":0.01058125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007082296463329534,"score_gpt":0.2423381494468425,"score_spread":0.235255852983513,"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."}}