{"id":"W4408038439","doi":"10.18280/ts.420112","title":"UMS-Net++: Modified SwinTransformer for Cardiac MRI Segmentation and Classification","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Net (polyhedron); Computer science; Artificial intelligence; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007734004,0.001897265,0.000789099,0.0008281896,0.0003952094,0.0009895755,0.001672636,0.001349041,0.02184933],"category_scores_gemma":[0.001623983,0.0007530097,0.0008538215,0.0007951493,0.0002633187,0.0007526736,0.001187782,0.001224138,0.01259116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003837607,"about_ca_system_score_gemma":0.001032991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004951523,"about_ca_topic_score_gemma":0.01685266,"domain_scores_codex":[0.9998196,0.00003539524,0.00001580906,0.00004253988,0.00006042131,0.00002624701],"domain_scores_gemma":[0.9996754,0.0001213154,0.00002657636,0.00004509887,0.00009928619,0.00003234106],"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":[0.0007506235,0.0001482603,0.0006899327,0.0003188858,0.0002070787,0.000195749,0.00008059952,0.04325217,0.03180647,0.002749039,0.0751471,0.8446541],"study_design_scores_gemma":[0.0001686594,0.0001996634,0.001258616,0.00005359311,0.0001016379,0.0005466738,0.00004212136,0.8870376,0.05360415,0.007615534,0.04928821,0.00008353521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003426822,0.0004834557,0.9651033,0.0002145181,0.0001865786,0.000100486,0.002025287,0.02695554,0.001503983],"genre_scores_gemma":[0.03632214,0.000590821,0.9367325,0.000622347,0.0001611371,0.0005189856,0.006082498,0.003498067,0.01547145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02184933,"threshold_uncertainty_score":0.0730933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584436495219423,"score_gpt":0.3016761311101981,"score_spread":0.2858317661580039,"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."}}