{"id":"W4391066052","doi":"10.1016/j.jhepr.2024.101008","title":"Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning","year":2024,"lang":"en","type":"article","venue":"JHEP Reports","topic":"AI in cancer detection","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Assistance publique-Hôpitaux de Paris","keywords":"Artificial intelligence; Hepatocellular carcinoma; Liver cancer; Cluster analysis; Intrahepatic Cholangiocarcinoma; Computer science; Medicine; Cancer; Pathology; Biopsy; Convolutional neural network; Pattern recognition (psychology); Radiology; Internal medicine","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":[0.0003628076,0.0001764505,0.0001822265,0.0001456972,0.0001878455,0.0003723369,0.0002393118,0.0001069612,0.0001126383],"category_scores_gemma":[0.00003608352,0.0001751386,0.00009231422,0.0005822897,0.00003569591,0.0008526077,0.0001731846,0.0003209698,0.00003390927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000692763,"about_ca_system_score_gemma":0.0002697046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001677039,"about_ca_topic_score_gemma":0.00004950035,"domain_scores_codex":[0.9980234,0.0000784504,0.0004307077,0.0007902804,0.0004121659,0.0002650077],"domain_scores_gemma":[0.9990322,0.00006550259,0.0002080281,0.000508496,0.0001004209,0.00008537767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000197082,0.00006568625,0.03276807,0.0001779334,0.0001397451,0.002866987,0.003128093,0.005103402,0.4611667,0.0004507905,0.0007984514,0.4933144],"study_design_scores_gemma":[0.00007786748,0.00002715298,0.09466978,0.0001759147,0.00004419589,0.0005458408,0.00004271469,0.8876787,0.007475051,0.0005695777,0.008375536,0.0003175986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5505118,0.004281464,0.4389558,0.0004489923,0.003598951,0.0002089688,0.000002425775,0.0008216932,0.001169974],"genre_scores_gemma":[0.980086,0.0001198708,0.01861524,0.00009129966,0.0006259723,0.00003796868,0.00001630309,0.00003207577,0.0003752759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8825754,"threshold_uncertainty_score":0.7141945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03249325826326052,"score_gpt":0.2709599125736169,"score_spread":0.2384666543103564,"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."}}