{"id":"W4415599542","doi":"10.2139/ssrn.5669479","title":"ETU-SAM: Efficient and Transparent Uncertainty Estimation for Segment Anything Model in Ultrasound Segmentation","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Estimator; Key (lock); Bayesian inference; Bayesian probability; Inference; Uncertainty quantification; Software deployment; Reliability (semiconductor)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003993103,0.001932396,0.003015646,0.00236593,0.001052843,0.002664774,0.003440694,0.003342451,0.005408377],"category_scores_gemma":[0.01139588,0.001527998,0.002283858,0.002208243,0.0009722354,0.003378025,0.005383654,0.003638186,0.003023697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008843995,"about_ca_system_score_gemma":0.001855138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004093288,"about_ca_topic_score_gemma":0.005937125,"domain_scores_codex":[0.9977725,0.0006210509,0.0001637388,0.0005214593,0.0007356245,0.000185539],"domain_scores_gemma":[0.9966109,0.001685123,0.0002460102,0.0007913923,0.0005339117,0.000132547],"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":[0.001002785,0.0001981812,0.001367279,0.0002935466,0.000293906,0.0001768346,0.0002096737,0.1564417,0.02134575,0.01204292,0.01541968,0.7912079],"study_design_scores_gemma":[0.0000193223,0.0000369645,0.0002003969,0.00001274681,0.00002201346,0.00006415493,0.00001984287,0.9840549,0.006158401,0.007618888,0.001774216,0.00001817231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002332759,0.0001687596,0.9941154,0.0000788405,0.00003912663,0.00003632404,0.0001956207,0.002874161,0.0001589563],"genre_scores_gemma":[0.07614242,0.0002130111,0.9187167,0.0001735005,0.0001329643,0.0002197427,0.001572092,0.001153414,0.001676245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005408377,"threshold_uncertainty_score":0.02111781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02628206094017874,"score_gpt":0.3113857596081905,"score_spread":0.2851036986680118,"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."}}