{"id":"W2607586613","doi":"","title":"BOUNDARY POINT DETECTION FOR ULTRASOUND IMAGE SEGMENTATION USING GUMBEL DISTRIBUTIONS","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Context (archaeology); Gumbel distribution; Boundary (topology); Artificial intelligence; Segmentation; Image segmentation; Noise (video); Computer vision; Computer science; Intensity (physics); Contrast (vision); Ultrasound; Point (geometry); Image (mathematics); Mathematics; Pattern recognition (psychology); Physics; Acoustics; Geometry; Optics; Geology; Statistics; Mathematical analysis","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":[0.003838607,0.0008755095,0.001330675,0.002898732,0.0007368466,0.001777555,0.001436076,0.002474497,0.001451557],"category_scores_gemma":[0.01808611,0.0009143144,0.001157571,0.001745002,0.002121346,0.002884872,0.001786445,0.002307558,0.000927689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001310623,"about_ca_system_score_gemma":0.000802675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002273413,"about_ca_topic_score_gemma":0.001853206,"domain_scores_codex":[0.9977513,0.0009115805,0.0001027107,0.0004226733,0.0006972919,0.0001144562],"domain_scores_gemma":[0.9901519,0.007630965,0.0007972518,0.0005372148,0.0007118121,0.0001708031],"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":[0.0005247575,0.0001297068,0.004142365,0.0002322648,0.0001720205,0.0003341814,0.0006511053,0.6369449,0.0352994,0.04854259,0.001783102,0.2712436],"study_design_scores_gemma":[0.000005368557,0.00002431145,0.0004034992,0.0000140214,0.000006590779,0.00006363581,0.0000134068,0.981846,0.002044572,0.01524187,0.0003204706,0.00001620011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006622607,0.0001983435,0.992547,0.00006183757,0.000007286389,0.0000181158,0.000009003093,0.0002823264,0.0002535732],"genre_scores_gemma":[0.3560422,0.0006404998,0.6413562,0.000181947,0.00007107198,0.0001320498,0.0001465025,0.0002008682,0.001228556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003838607,"threshold_uncertainty_score":0.02030075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02174296748190494,"score_gpt":0.3266137663663065,"score_spread":0.3048707988844015,"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."}}