{"id":"W4399117207","doi":"10.1117/12.3025631","title":"Recovering unprocessed digital mammograms from processed mammograms for quantitative analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Hospital","funders":"","keywords":"Artificial intelligence; Mammography; Pattern recognition (psychology); Computer science; Mean squared error; Artificial neural network; Mathematics; Digital mammography; Similarity (geometry); Computer vision; Breast cancer; Statistics; Medicine; Image (mathematics)","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":[0.001138477,0.001062942,0.000511092,0.00222108,0.0001618957,0.0009273391,0.000482403,0.0006514505,0.004815747],"category_scores_gemma":[0.004360469,0.0003579535,0.0006244509,0.00114289,0.000376978,0.0007835974,0.0004595041,0.0003798616,0.001476648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002446999,"about_ca_system_score_gemma":0.0003899561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006987206,"about_ca_topic_score_gemma":0.000908859,"domain_scores_codex":[0.9997209,0.00003935266,0.00002707722,0.0000650486,0.000115875,0.00003186171],"domain_scores_gemma":[0.9986044,0.0005582074,0.0001282599,0.0002902089,0.0003931042,0.00002595834],"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":[0.001265856,0.0002026062,0.005990764,0.000841734,0.000146398,0.0004331557,0.0001618015,0.02753506,0.417227,0.00141928,0.001555233,0.5432211],"study_design_scores_gemma":[0.00009112296,0.0009682724,0.05378651,0.0001012731,0.0003559004,0.002713731,0.0004360287,0.4367021,0.4890722,0.004641449,0.01102141,0.0001099179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4078853,0.001590914,0.5788735,0.0003056652,0.0001980565,0.000346333,0.001898894,0.004839261,0.004062192],"genre_scores_gemma":[0.4863249,0.000794234,0.5083363,0.00008015393,0.00007783706,0.0001259952,0.001668809,0.000457771,0.002133897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004815747,"threshold_uncertainty_score":0.0161103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02027507646808652,"score_gpt":0.285444084173996,"score_spread":0.2651690077059095,"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."}}