{"id":"W4229451765","doi":"10.3390/jimaging8050131","title":"BI-RADS BERT and Using Section Segmentation to Understand Radiology Reports","year":2022,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Topic Modeling","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"Simon Fraser University; Compute Canada; Canadian Institutes of Health Research; Sunnybrook Research Institute","keywords":"Computer science; Segmentation; Artificial intelligence; Lexicon; Sentence; Natural language processing; Breast imaging; Section (typography); Classifier (UML); Mammography; Breast cancer; Pattern recognition (psychology); Medicine; Cancer","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001106554,0.001104299,0.0003091148,0.001321704,0.0002591286,0.001166975,0.0007200975,0.0008678995,0.003072529],"category_scores_gemma":[0.004202924,0.0004650588,0.0007716601,0.0004786722,0.0003719203,0.003723847,0.001030519,0.001048193,0.003803108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008088459,"about_ca_system_score_gemma":0.0009612197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007635999,"about_ca_topic_score_gemma":0.009459048,"domain_scores_codex":[0.9992749,0.0002031342,0.00008133875,0.000221858,0.0001530029,0.00006581287],"domain_scores_gemma":[0.9982008,0.0008264722,0.0002098414,0.0002889709,0.0004188403,0.00005514154],"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":[0.00103285,0.0003595876,0.02281934,0.0008567336,0.000147945,0.0008442696,0.001650134,0.1003903,0.09869079,0.01384549,0.03676425,0.7225983],"study_design_scores_gemma":[0.00003483353,0.0003030128,0.01047069,0.0001282596,0.00009741551,0.0007918689,0.0004678168,0.8735829,0.05580641,0.008443163,0.04977708,0.00009655958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1989821,0.001282315,0.7402887,0.001361545,0.0004315301,0.0005411412,0.006900955,0.03928971,0.01092201],"genre_scores_gemma":[0.5536898,0.0006841848,0.4131948,0.0004170901,0.000138993,0.0003260167,0.01983872,0.001301118,0.01040934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007635999,"threshold_uncertainty_score":0.01518315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02553176897951776,"score_gpt":0.2778159636013181,"score_spread":0.2522841946218003,"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."}}