{"id":"W4362694298","doi":"10.1117/12.2653918","title":"Predicting Crohn’s disease severity in the colon using mixed cell nucleus density from pseudo labels","year":2023,"lang":"en","type":"article","venue":"","topic":"Inflammatory Bowel Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"National Center for Advancing Translational Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; Vanderbilt Institute for Clinical and Translational Research; National Institute of General Medical Sciences; National Center for Research Resources; Nvidia; Georgia Clinical and Translational Science Alliance; Leona M. and Harry B. Helmsley Charitable Trust; Vanderbilt University Medical Center; Patient-Centered Outcomes Research Institute; U.S. Department of Veterans Affairs; Vanderbilt University; National Institutes of Health; National Science Foundation","keywords":"Histogram; Nucleus; Biopsy; H&E stain; Pathology; Inflammatory bowel disease; Disease; Crohn's disease; Artificial intelligence; Medicine; Pattern recognition (psychology); Computer science; Immunohistochemistry; 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":[0.001260595,0.0005332995,0.0005392935,0.001505835,0.0002009938,0.0008560895,0.0002527765,0.0005757317,0.0006769403],"category_scores_gemma":[0.002878515,0.0002020286,0.000586523,0.000638731,0.0003217358,0.0004425664,0.0005240841,0.0004955988,0.0004399111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002971661,"about_ca_system_score_gemma":0.0002143955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002436961,"about_ca_topic_score_gemma":0.003656282,"domain_scores_codex":[0.9994886,0.0001362534,0.00004088964,0.0001604898,0.0001103375,0.00006342844],"domain_scores_gemma":[0.9986367,0.0006436294,0.0002230652,0.0001355599,0.0002449478,0.0001160922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002785173,0.0002734847,0.6869195,0.0002182378,0.0003213727,0.0004717394,0.0002965414,0.06410586,0.0869031,0.0003123278,0.001551619,0.1558411],"study_design_scores_gemma":[0.00005021619,0.0005965125,0.3513654,0.00005993629,0.0001210635,0.0008655021,0.0002784528,0.6175001,0.02694526,0.001045522,0.001107366,0.00006465213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843156,0.000322337,0.01395415,0.00005546117,0.00001805432,0.00002489598,0.0005407105,0.000287333,0.0004814042],"genre_scores_gemma":[0.9851424,0.00009502333,0.01298665,0.00002380981,0.00001203164,0.00001993686,0.001408514,0.00001767769,0.0002939384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002436961,"threshold_uncertainty_score":0.00666672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01324827943990941,"score_gpt":0.2397033825528116,"score_spread":0.2264551031129022,"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."}}