{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006570996,0.0000793113,0.0001060167,0.00004596002,0.00008345564,0.00005188141,0.0001442116,0.00005400077,0.000006432093],"category_scores_gemma":[0.00001316513,0.00008205573,0.00002478742,0.0002126836,0.0001308181,0.0008390092,0.0000782132,0.00007607533,0.000004941695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006782684,"about_ca_system_score_gemma":0.0002386798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000323558,"about_ca_topic_score_gemma":0.0001192649,"domain_scores_codex":[0.9992913,0.00001725109,0.0001469725,0.0002661466,0.0001241983,0.00015414],"domain_scores_gemma":[0.9995614,0.00003546338,0.0001098716,0.0001920041,0.00006214087,0.00003910554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001115812,0.0000496044,0.3231346,0.0002510443,0.0001094697,0.000009664151,0.008406341,0.05582117,0.3104929,0.001480511,0.001799694,0.2983334],"study_design_scores_gemma":[0.007469662,0.001479204,0.302796,0.000369129,0.00006027248,0.0001409527,0.002330626,0.2269493,0.436603,0.001480949,0.01939423,0.0009266132],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9082003,0.0003653319,0.08916439,0.0001220687,0.001399883,0.0002658484,0.0001944252,0.0000811941,0.0002065177],"genre_scores_gemma":[0.9981914,0.0002310783,0.001299545,0.00005861081,0.00004893963,0.00007153714,0.00000858177,0.000005468137,0.00008487486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2974068,"threshold_uncertainty_score":0.3346135,"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."}}