{"id":"W4417348244","doi":"10.1117/1.jmi.12.6.061401","title":"Introduction to the JMI Special Section on Computational Pathology","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Special section; Section (typography); Medical imaging; Computed tomography","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":[0.003633932,0.002745307,0.002296455,0.008460343,0.001080883,0.004877886,0.002304375,0.002876751,0.05958239],"category_scores_gemma":[0.008141468,0.001286873,0.002546079,0.005130398,0.001827304,0.005329715,0.004261317,0.007366271,0.05436698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001798907,"about_ca_system_score_gemma":0.002103841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001118605,"about_ca_topic_score_gemma":0.002123818,"domain_scores_codex":[0.9974661,0.0004882231,0.0003301482,0.0006155544,0.0009291603,0.0001707302],"domain_scores_gemma":[0.9904918,0.002812805,0.0005369494,0.001410665,0.003342457,0.001405314],"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":[0.00003471237,0.00007616274,0.0002865724,0.000571764,0.00004602635,0.00009100894,0.00003990176,0.001060547,0.001200557,0.01215653,0.7960898,0.1883465],"study_design_scores_gemma":[0.00000873192,0.0000789639,0.0009055885,0.0004543159,0.0000296277,0.0006887522,0.00002478896,0.002174719,0.0006153162,0.01898881,0.9759729,0.00005751795],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.001272693,0.1848211,0.3093104,0.05181087,0.3569696,0.0004113108,0.002442899,0.004608564,0.08835261],"genre_scores_gemma":[0.007747381,0.1161969,0.1013467,0.01911996,0.571847,0.0005567918,0.004089652,0.004965452,0.1741301],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.05958239,"threshold_uncertainty_score":0.1993229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006878304536962735,"score_gpt":0.2903145322760559,"score_spread":0.2834362277390932,"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."}}