{"id":"W4414161448","doi":"10.1109/tmi.2025.3609319","title":"MultiASNet: Multimodal Label Noise Robust Framework for the Classification of Aortic Stenosis in Echocardiography","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Noise (video); Inference; Doppler effect; Doppler ultrasound; Classifier (UML); Pattern recognition (psychology); Stenosis; Cardiac imaging","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002913961,0.002268399,0.001807798,0.001771928,0.0008660578,0.00139683,0.003135813,0.003145744,0.002209057],"category_scores_gemma":[0.004565986,0.0005375323,0.001676024,0.0009406154,0.0008572437,0.00158807,0.001741736,0.002768335,0.001568671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002265505,"about_ca_system_score_gemma":0.001835349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01751302,"about_ca_topic_score_gemma":0.02392047,"domain_scores_codex":[0.9985474,0.0004522509,0.00006873538,0.0005556981,0.0002228788,0.0001531641],"domain_scores_gemma":[0.9987875,0.0005463948,0.0001203811,0.0001275705,0.0003198732,0.00009831844],"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":[0.001396398,0.001038514,0.01285215,0.0004066147,0.0005232575,0.0005597866,0.0003092827,0.387675,0.009579743,0.00560313,0.04905225,0.531004],"study_design_scores_gemma":[0.00002306133,0.00007756621,0.0004936625,0.00002932153,0.00002829541,0.00004310816,0.00003432637,0.992371,0.00140771,0.003728869,0.001747751,0.0000153248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1256921,0.005854761,0.8349076,0.003112343,0.0007992106,0.0004146706,0.006021193,0.01902063,0.00417744],"genre_scores_gemma":[0.6917261,0.001228509,0.2734006,0.002610204,0.0007148113,0.0006093819,0.01780567,0.0006977702,0.01120698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01751302,"threshold_uncertainty_score":0.03482217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02462017537556388,"score_gpt":0.3259536098347345,"score_spread":0.3013334344591706,"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."}}