{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001897154,0.0001831819,0.0002152592,0.0002108009,0.00006513713,0.00008906918,0.0001416775,0.0001202687,5.562102e-7],"category_scores_gemma":[0.00004820354,0.0001336719,0.00003192699,0.00007687739,0.0002341021,0.001732974,0.0001222316,0.0002888867,1.068335e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005996769,"about_ca_system_score_gemma":0.00001689085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003159271,"about_ca_topic_score_gemma":0.00001431859,"domain_scores_codex":[0.9991219,0.000002535061,0.0003999941,0.0001303585,0.0001707841,0.0001744195],"domain_scores_gemma":[0.9995294,0.0001027869,0.0001955672,0.0001032926,0.00002619616,0.00004278373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001585729,0.000003377952,0.00001225324,0.0006868188,0.000004765971,4.313774e-7,0.0003417339,0.00003653467,0.0001291002,0.0006962569,1.589181e-7,0.9980727],"study_design_scores_gemma":[0.0005628018,0.0009174939,0.0002670276,0.00183621,0.00003073982,0.0001968935,0.0000877203,0.3859864,0.003509341,0.00239705,0.6034684,0.0007399394],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00009287843,0.02018137,0.978416,0.00008291516,0.0001715445,0.0004122665,0.00001300184,0.0001081599,0.0005219194],"genre_scores_gemma":[0.4797364,0.07100203,0.443624,0.0001019115,0.0006458535,0.0004646343,0.0001179257,0.0001749128,0.004132351],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9973328,"threshold_uncertainty_score":0.5450981,"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."}}