{"id":"W1147193425","doi":"10.1109/tmi.2015.2470529","title":"Triaging Diagnostically Relevant Regions from Pathology Whole Slides of Breast Cancer: A Texture Based Approach","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Kela; Sunnybrook Research Institute","keywords":"Breast cancer; Digital pathology; Texture (cosmology); Pathology; Computer science; Medicine; Artificial intelligence; Cancer; Computer vision; Image (mathematics); Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006225377,0.0002264073,0.0003588382,0.0002487817,0.0001544953,0.00006303681,0.0007718975,0.0001524579,0.00006054502],"category_scores_gemma":[0.0001404245,0.0002049772,0.0001435023,0.0006416804,0.0003028624,0.0003908331,0.0000101142,0.0007435863,0.00001879628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002265611,"about_ca_system_score_gemma":0.0006649456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005879963,"about_ca_topic_score_gemma":0.0000736322,"domain_scores_codex":[0.9972826,0.0002787981,0.0004680238,0.0006417565,0.0009383921,0.0003904332],"domain_scores_gemma":[0.9977894,0.0007371032,0.00014924,0.0006604594,0.0002070457,0.0004567518],"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":[0.0001871424,0.0008973944,0.0004468234,0.00006141075,0.00009556673,0.0002624079,0.001978476,0.02484897,0.002990792,0.0003567633,0.00401305,0.9638612],"study_design_scores_gemma":[0.002536705,0.00009083892,0.0006699357,0.0004149365,0.00009797386,0.0002686786,0.0002488255,0.9858978,0.005768609,0.001953695,0.001660842,0.0003911614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001487909,0.0003411605,0.977518,0.01848743,0.001429719,0.0001969528,0.00007562579,0.000271704,0.0001915639],"genre_scores_gemma":[0.9642372,0.00009719578,0.03367335,0.001600408,0.0001962759,0.0001350675,0.000004298869,0.00002794835,0.0000282758],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.96347,"threshold_uncertainty_score":0.8358727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01958976026000064,"score_gpt":0.2679687745154472,"score_spread":0.2483790142554465,"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."}}