{"id":"W3029272675","doi":"10.1038/s41379-020-0540-1","title":"Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media","year":2020,"lang":"en","type":"article","venue":"Modern Pathology","topic":"AI in cancer detection","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; Health Sciences Centre; Spinal Cord Injury BC","funders":"National Institute of General Medical Sciences; U.S. Department of Health and Human Services; National Cancer Institute; National Institutes of Health; Chiba University; Weill Cornell Medical College","keywords":"Pathology; Digital pathology; Social media; Medicine; Computer science; Artificial intelligence; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004827597,0.0002855773,0.0004435028,0.0001402096,0.0003423122,0.00007777321,0.0008454802,0.0002490682,0.00005035327],"category_scores_gemma":[0.0004506659,0.0003060933,0.0001369595,0.0002389495,0.0001697486,0.0002609078,0.0004671943,0.0005422472,0.0003086797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001498746,"about_ca_system_score_gemma":0.0001222292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001228646,"about_ca_topic_score_gemma":0.00001249354,"domain_scores_codex":[0.9969096,0.0005347555,0.0003557069,0.001152123,0.0003083372,0.000739514],"domain_scores_gemma":[0.9985309,0.0005176617,0.0001417223,0.000432322,0.0001424846,0.0002349363],"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":[0.0008241198,0.0001619609,0.0003758745,0.00007604073,0.0000372123,0.0009060982,0.0279696,0.008383078,0.1177318,0.001401202,0.001671707,0.8404614],"study_design_scores_gemma":[0.001269238,0.0008471352,0.004436469,0.0000129419,0.00002812827,0.00005752973,0.00007947212,0.9814031,0.005087241,0.004386386,0.001927471,0.0004649186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1498045,0.000108932,0.8428845,0.005188647,0.0005937848,0.0004579704,0.00002274585,0.0005423723,0.0003965087],"genre_scores_gemma":[0.9811791,0.00004338825,0.01607142,0.00177622,0.0004796308,0.0002007775,0.00003385319,0.00005509949,0.0001604884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.97302,"threshold_uncertainty_score":0.9999391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03251994904213234,"score_gpt":0.2930630265485612,"score_spread":0.2605430775064289,"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."}}