{"id":"W7117111041","doi":"10.1016/j.jpi.2025.100537","title":"ADPv2: A hierarchical histological tissue type-annotated dataset for potential biomarker discovery of colorectal disease","year":2025,"lang":"en","type":"article","venue":"Journal of Pathology Informatics","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Sunnybrook Health Science Centre; Centre Hospitalier de l’Université de Montréal; Institute for Research in Immunology and Cancer; Cegep Edouard Montpetit; Canada Research Chairs; University of Toronto; Université de Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Economic Development, Job Creation and Trade","keywords":"Digital pathology; Biomarker discovery; Annotation; Colorectal cancer; Biomarker; Biopsy","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":[0.0008693119,0.001451046,0.0007718079,0.00327593,0.0006630025,0.001239878,0.002651148,0.002204185,0.005078264],"category_scores_gemma":[0.003348497,0.0004998597,0.001258139,0.002545572,0.0005003461,0.0006988558,0.001832996,0.001355596,0.003444473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001205147,"about_ca_system_score_gemma":0.001616501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01315063,"about_ca_topic_score_gemma":0.03014007,"domain_scores_codex":[0.9990737,0.0001260132,0.0000799008,0.0003698159,0.0002537049,0.00009676337],"domain_scores_gemma":[0.9985967,0.0004441177,0.0001455215,0.0003994767,0.0002594073,0.0001548469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00148183,0.0009098587,0.05363005,0.004339663,0.0006708481,0.001887691,0.0003185399,0.0285076,0.03577705,0.003666951,0.6532953,0.2155146],"study_design_scores_gemma":[0.001094284,0.0008892634,0.1897533,0.001051064,0.0006123607,0.009313107,0.0009206887,0.2074506,0.05051569,0.01306438,0.5250029,0.0003323444],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1254399,0.004349146,0.03911302,0.001533739,0.0004511969,0.0009459148,0.8042666,0.01542164,0.008478904],"genre_scores_gemma":[0.09449218,0.000609125,0.04012576,0.0003970966,0.00007104282,0.000518427,0.8607258,0.0003598282,0.002700813],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01315063,"threshold_uncertainty_score":0.0261482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01545069576117428,"score_gpt":0.2988720792633532,"score_spread":0.2834213835021789,"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."}}