{"id":"W4391467761","doi":"10.1002/mp.16968","title":"TPA: Two‐stage progressive attention segmentation framework for hepatocellular carcinoma on multi‐modality MRI","year":2024,"lang":"en","type":"article","venue":"Medical Physics","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Modality (human–computer interaction); Computer science; Hepatocellular carcinoma; Artificial intelligence; Magnetic resonance imaging; Pattern recognition (psychology); Stage (stratigraphy); Correlation; Medicine; Radiology; Mathematics","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":[0.001584901,0.001145057,0.001067931,0.001435217,0.0005492842,0.0009068241,0.002415614,0.001835739,0.001800114],"category_scores_gemma":[0.002119639,0.0005132361,0.001550011,0.0007203237,0.0007241336,0.001119163,0.001498442,0.001280103,0.0004705843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001427944,"about_ca_system_score_gemma":0.001702611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01238633,"about_ca_topic_score_gemma":0.01092744,"domain_scores_codex":[0.9993668,0.0001332994,0.00002886301,0.0002171301,0.0001421324,0.0001118104],"domain_scores_gemma":[0.9993138,0.0002654796,0.00008514988,0.00006100089,0.0001997203,0.00007474748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005056373,0.0002594188,0.004021284,0.0002175639,0.000260374,0.0004359232,0.0002588615,0.5101663,0.0259364,0.006353518,0.004633851,0.4469509],"study_design_scores_gemma":[0.000009936858,0.00005791823,0.0003602192,0.000005724386,0.00002846902,0.00006441089,0.000008997105,0.9947049,0.002102214,0.002171627,0.0004775254,0.000008009703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03740285,0.001154277,0.9575833,0.0005500905,0.00007327201,0.000191691,0.0001400711,0.001603233,0.001301234],"genre_scores_gemma":[0.7451682,0.0007464358,0.247729,0.000588326,0.0002727977,0.0003285427,0.0005694067,0.0002432371,0.00435409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01238633,"threshold_uncertainty_score":0.02462846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07549850575509592,"score_gpt":0.3385746008525532,"score_spread":0.2630760950974573,"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."}}