{"id":"W4389542671","doi":"10.1109/embc40787.2023.10340968","title":"Domain-Adversarial Transformer Network for Multiphase Liver Tumor Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Research and Development; National Natural Science Foundation of China","keywords":"Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Image segmentation; Feature (linguistics); Feature extraction; Computer vision; Adversarial system; Transformer; Engineering","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":[0.0001424313,0.0001115966,0.00009971068,0.00005011686,0.0002516888,0.00004893619,0.0003647205,0.00002799529,0.00002128433],"category_scores_gemma":[0.00000551916,0.0001023932,0.00007651804,0.0007204908,0.00002656259,0.0004274049,0.00003972892,0.00005508056,0.0001880348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003299756,"about_ca_system_score_gemma":0.00002309796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000733429,"about_ca_topic_score_gemma":0.00002657751,"domain_scores_codex":[0.998952,0.00001884991,0.0001865841,0.0003384241,0.0001451495,0.0003589359],"domain_scores_gemma":[0.9993103,0.0002388958,0.00004964635,0.0002760526,0.00004401912,0.00008104232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001203565,0.0001211617,0.0001323708,0.00003284783,0.00004840535,0.00002463201,0.001348889,0.1401732,0.01616388,0.5765123,0.09799291,0.1673291],"study_design_scores_gemma":[0.00295929,0.0001643297,0.0004621526,0.00001137944,0.00001702822,0.00001263604,0.0001025911,0.756024,0.008689902,0.06230945,0.1687776,0.0004696406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00358083,0.0000148845,0.9930916,0.001000242,0.0003718438,0.0008812398,0.000007947257,0.0005708167,0.0004805323],"genre_scores_gemma":[0.02804228,0.00002446337,0.9685365,0.001176893,0.0005083338,0.0005583582,0.00005261736,0.00001910455,0.001081432],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6158508,"threshold_uncertainty_score":0.4175473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02570716671781985,"score_gpt":0.287630665403569,"score_spread":0.2619234986857492,"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."}}