{"id":"W2486659465","doi":"10.4018/978-1-4666-0059-1.ch002","title":"Digital Image Processing and Machine Learning Techniques for the Detection of Architectural Distortion in Prior Mammograms","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in bioinformatics and biomedical engineering book series","topic":"AI in cancer detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Western University","funders":"","keywords":"Artificial intelligence; Receiver operating characteristic; Mammography; Pattern recognition (psychology); Computer science; Distortion (music); Computer vision; Fractal dimension; Mathematics; Fractal; Breast cancer; Bandwidth (computing); Cancer; Medicine; Machine learning","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":[0.002176301,0.0007405783,0.0006799366,0.003030083,0.0002581107,0.0006759411,0.0006916721,0.0005948999,0.001992665],"category_scores_gemma":[0.005579232,0.0003392509,0.0006359996,0.001825514,0.0005281712,0.0008319455,0.0005097822,0.0009054797,0.001025037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00053163,"about_ca_system_score_gemma":0.0006178254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001570038,"about_ca_topic_score_gemma":0.002432829,"domain_scores_codex":[0.9990851,0.0001985156,0.00008428605,0.0001278812,0.0004708978,0.00003328468],"domain_scores_gemma":[0.9982924,0.0009120188,0.0002017306,0.0001889864,0.0003737661,0.00003105065],"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":[0.000125476,0.0001070864,0.003352421,0.000305154,0.0001011446,0.0001688732,0.00008190018,0.02625586,0.02958806,0.004277979,0.003236429,0.9323996],"study_design_scores_gemma":[0.00003941979,0.0002047938,0.01863181,0.0001350287,0.0001075754,0.000909618,0.00008522311,0.9122344,0.04481539,0.01071334,0.01203009,0.00009334719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03242348,0.003630923,0.9584547,0.0004358232,0.0001071849,0.0001205811,0.0001928482,0.002236347,0.002398188],"genre_scores_gemma":[0.191982,0.002382758,0.8030459,0.0001584679,0.00009609495,0.000179938,0.0003303823,0.00008722983,0.001737361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003030083,"threshold_uncertainty_score":0.01150954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004951593436227615,"score_gpt":0.2066101291387141,"score_spread":0.2016585357024865,"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."}}