{"id":"W4366831358","doi":"10.1016/j.media.2023.102826","title":"Calibrating segmentation networks with margin-based label smoothing","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; École de Technologie Supérieure","funders":"H2020 Marie Skłodowska-Curie Actions; Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Softmax function; Discriminative model; Computer science; Margin (machine learning); Segmentation; Artificial intelligence; Machine learning; Logit; Artificial neural network; Mathematical optimization; Pattern recognition (psychology); Mathematics","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":[0.0004042549,0.0001336164,0.0002168352,0.000261601,0.0002345865,0.0001507595,0.0006154751,0.00005916994,0.00009430244],"category_scores_gemma":[0.0000962269,0.000107669,0.00008259674,0.006772683,0.00008562837,0.0003973047,0.0001470738,0.0002280925,0.00005337234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003303418,"about_ca_system_score_gemma":0.0000593874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002915763,"about_ca_topic_score_gemma":0.00006682531,"domain_scores_codex":[0.9981435,0.00009493187,0.0002676683,0.000450595,0.0006975677,0.00034572],"domain_scores_gemma":[0.9986781,0.0004134113,0.0001190211,0.0005043957,0.00006845237,0.0002166376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000139027,0.0001516486,0.01089695,0.00002683975,0.0006496721,0.0004745761,0.0003293096,0.7381834,0.001447728,0.002462155,0.004717118,0.2406467],"study_design_scores_gemma":[0.0002559761,0.00001718047,0.001092972,0.00001297349,0.0001151144,0.000001552628,0.00002095949,0.997699,0.0003478779,0.0001407217,0.0001643625,0.0001313686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005793924,0.00002522747,0.9880047,0.005311813,0.00003042571,0.0001079779,0.000001238848,0.0005294941,0.0001952393],"genre_scores_gemma":[0.5337835,0.00003336554,0.4609928,0.004278248,0.0001982171,0.0001667863,0.0001924596,0.00003118324,0.0003233865],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5279896,"threshold_uncertainty_score":0.4390613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01303880553543688,"score_gpt":0.2853663443026501,"score_spread":0.2723275387672132,"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."}}