{"id":"W4402029867","doi":"10.1088/2057-1976/ad7594","title":"Automatic segmentation of echocardiographic images using a shifted windows vision transformer architecture","year":2024,"lang":"en","type":"article","venue":"Biomedical Physics & Engineering Express","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Segmentation; Computer vision; Architecture; Artificial intelligence; Computer science; Transformer; Computer graphics (images); Engineering; Geography; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008745827,0.0002413508,0.0002681394,0.000236702,0.00003056125,0.00004242165,0.0001381659,0.00008503514,0.00001455374],"category_scores_gemma":[0.000007541033,0.0002284654,0.0001668654,0.0006789804,0.00006581845,0.0002952659,0.00001539232,0.0002924621,0.000003515247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004695921,"about_ca_system_score_gemma":0.00001558068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003482664,"about_ca_topic_score_gemma":1.869866e-8,"domain_scores_codex":[0.9988517,0.00001228296,0.0002952129,0.0002058594,0.0003352187,0.000299719],"domain_scores_gemma":[0.9996013,0.00007871053,0.00001907536,0.0001771449,0.00001773647,0.000106053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002373479,0.00001930921,0.000004106791,0.0009636226,0.00009937913,0.00001041782,0.0003751038,0.180496,0.7791789,0.00008882932,0.0000430024,0.03871903],"study_design_scores_gemma":[0.0003276946,0.00003745748,0.0001288969,0.001123015,0.00009704083,0.0000126554,0.00003583683,0.9286578,0.06339722,0.0009472837,0.004873694,0.0003614315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1330799,0.001232292,0.8642485,0.00001500853,0.0004593367,0.0001642699,0.00003975806,0.0007403998,0.00002058792],"genre_scores_gemma":[0.8936164,0.00004637981,0.1059522,0.000005769498,0.0002400269,0.00002430345,0.00003644604,0.00007623422,0.000002289779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7605364,"threshold_uncertainty_score":0.9316547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005090818104896301,"score_gpt":0.2346180877353112,"score_spread":0.2295272696304149,"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."}}