{"id":"W3121030001","doi":"10.1007/s10278-020-00407-0","title":"DeepCAT: Deep Computer-Aided Triage of Screening Mammography","year":2021,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Triage; Mammography; Medicine; Artificial intelligence; Deep learning; Medical physics; Breast cancer; Radiology; Digital mammography; Prioritization; Breast cancer screening; Computer science; Cancer; Medical emergency; Internal 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001958493,0.0001072172,0.0003754903,0.0002806073,0.00003740562,0.00009656318,0.00006854716,0.00002828475,0.00001873776],"category_scores_gemma":[0.0001662185,0.0000961757,0.0003737308,0.0004579465,0.00004913361,0.0004682733,0.00004828371,0.0002465347,0.000001283494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003378836,"about_ca_system_score_gemma":0.00009102912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004438481,"about_ca_topic_score_gemma":0.000001648725,"domain_scores_codex":[0.998816,0.00002391463,0.0005187945,0.0001221409,0.000359247,0.0001599325],"domain_scores_gemma":[0.9987657,0.00009822637,0.0003709346,0.0001336339,0.0004898639,0.0001416332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001970494,0.0001622631,0.1026139,0.0001007573,0.0002830347,0.0009298909,0.0003186336,0.000187328,0.008049635,0.00001535395,0.0002420765,0.8851266],"study_design_scores_gemma":[0.03572302,0.01262058,0.5433302,0.006555287,0.002085073,0.05973474,0.005349702,0.04147065,0.2642997,0.003440091,0.02378073,0.001610257],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7574106,0.002581397,0.235915,0.0006679657,0.000416415,0.00006451051,0.000003928621,0.00002881235,0.002911291],"genre_scores_gemma":[0.9828123,0.00002352101,0.01651336,0.0001137298,0.0004756529,4.597765e-7,0.000003843691,0.00001546424,0.00004170518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8835164,"threshold_uncertainty_score":0.3921931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501457882796578,"score_gpt":0.2643472255568894,"score_spread":0.2493326467289236,"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."}}