{"id":"W2985641961","doi":"10.3390/cancers11111700","title":"Segmentation and Grade Prediction of Colon Cancer Digital Pathology Images Across Multiple Institutions","year":2019,"lang":"en","type":"article","venue":"Cancers","topic":"AI in cancer detection","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Digital pathology; Segmentation; Colorectal cancer; Digital image analysis; Cancer; Pathology; Medicine; Computer science; Artificial intelligence; Internal medicine; Computer vision","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":[0.00133186,0.0006849242,0.0004823299,0.004567638,0.000340911,0.001166454,0.0006045884,0.0009204574,0.000627113],"category_scores_gemma":[0.005361554,0.0003471593,0.0007127245,0.001874477,0.0002651102,0.0005154932,0.0009031223,0.0004641655,0.0006037169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009336006,"about_ca_system_score_gemma":0.0006725618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01333671,"about_ca_topic_score_gemma":0.02057101,"domain_scores_codex":[0.9990687,0.0001301239,0.0001018449,0.0002815148,0.0002602606,0.0001576466],"domain_scores_gemma":[0.9980016,0.0003860066,0.0003511381,0.0003070873,0.0007794656,0.0001746563],"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":[0.001018243,0.0002714726,0.6443247,0.000231371,0.0003676917,0.001044099,0.0003761138,0.04143769,0.03257775,0.0003424847,0.006014816,0.2719935],"study_design_scores_gemma":[0.00005748686,0.0003173762,0.4979807,0.00008683064,0.0003178499,0.001525792,0.0006533439,0.4547886,0.03810262,0.001110449,0.004990862,0.00006806081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9763156,0.00072435,0.01784989,0.0002650349,0.00004720388,0.0001453348,0.002066372,0.001414362,0.001171759],"genre_scores_gemma":[0.970456,0.0002492871,0.02433047,0.00005817914,0.00002577315,0.00003658306,0.004081871,0.00005472747,0.0007071113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01333671,"threshold_uncertainty_score":0.02651817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02358889150007935,"score_gpt":0.2938673322260061,"score_spread":0.2702784407259267,"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."}}