{"id":"W4407638182","doi":"10.1109/jbhi.2025.3542394","title":"Decouple-and-Couple Learning in Multi-Modal Brain Tumor Segmentation","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Modal; Artificial intelligence; Segmentation; Computer vision; Pattern recognition (psychology); Materials science","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":[0.001199398,0.00007113632,0.0001785618,0.0003337551,0.0001496603,0.00004454021,0.0000720251,0.00004485627,0.000008749733],"category_scores_gemma":[0.0004371524,0.00005721086,0.00002240766,0.0003163208,0.0001154995,0.0002328026,0.000017015,0.0003773201,0.000002638092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000819156,"about_ca_system_score_gemma":0.0002253704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001518662,"about_ca_topic_score_gemma":0.00001317868,"domain_scores_codex":[0.9985756,0.00006639961,0.0008985031,0.00005973449,0.0002351521,0.0001645656],"domain_scores_gemma":[0.9990854,0.0001955327,0.0004799272,0.00004061583,0.00003250066,0.0001660359],"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":[0.0002792103,0.0005901146,0.01060499,0.002006348,0.00002089389,0.00003318645,0.01615923,0.0003158256,0.05956809,0.00193497,0.007257879,0.9012293],"study_design_scores_gemma":[0.01128815,0.001944531,0.09984485,0.001123672,0.00002235923,0.001071429,0.01237555,0.7843558,0.01869267,0.001216349,0.06763759,0.0004271119],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9264342,0.0001348196,0.05025299,0.0222364,0.0006368739,0.0001916115,0.000002875577,0.0000223439,0.00008790394],"genre_scores_gemma":[0.9869091,0.0004817896,0.002545131,0.009860351,0.00004735425,0.000003330562,9.82702e-7,0.000003915817,0.0001480471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9008021,"threshold_uncertainty_score":0.2332991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05553657656354945,"score_gpt":0.3605128278684592,"score_spread":0.3049762513049097,"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."}}