{"id":"W4406264718","doi":"10.1109/cce62852.2024.10771056","title":"Evaluation of Segmentation Quality in Magnetic Resonance Images Using Singular Value Decomposition: A Feasibility Study","year":2024,"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 Calgary","funders":"","keywords":"Singular value decomposition; Decomposition; Magnetic resonance imaging; Segmentation; Quality (philosophy); Computer science; Image segmentation; Artificial intelligence; Computer vision; Nuclear magnetic resonance; Pattern recognition (psychology); Physics; Chemistry; Radiology; Medicine","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":[0.008635597,0.0007281704,0.000653996,0.002239648,0.0003551578,0.001204625,0.0007006427,0.001444261,0.0009550314],"category_scores_gemma":[0.02573776,0.0003098572,0.0006638676,0.001239619,0.0008736448,0.001095366,0.0008810314,0.0003331003,0.0003415934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004204043,"about_ca_system_score_gemma":0.0005274528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454575,"about_ca_topic_score_gemma":0.001160745,"domain_scores_codex":[0.9952694,0.002333034,0.0003650826,0.0006161953,0.001227947,0.000188293],"domain_scores_gemma":[0.9827121,0.01023335,0.001203753,0.001326946,0.004099425,0.0004244976],"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":[0.005860638,0.002203231,0.07162143,0.001576707,0.0004741032,0.001178532,0.002213174,0.09046584,0.4547076,0.002502382,0.001082722,0.3661136],"study_design_scores_gemma":[0.0001955606,0.01128661,0.07480985,0.0001204628,0.0003120821,0.002445878,0.0008440984,0.7412884,0.1654629,0.001575944,0.001497383,0.0001609256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7838425,0.0006297236,0.2130103,0.0001723144,0.00003963707,0.0005565183,0.0002026964,0.0004302298,0.00111627],"genre_scores_gemma":[0.8581746,0.0001900273,0.140901,0.00002365566,0.0000206147,0.0001012978,0.0002616667,0.00006328867,0.0002637101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008635597,"threshold_uncertainty_score":0.04566991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09619900559892551,"score_gpt":0.4561213757792483,"score_spread":0.3599223701803228,"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."}}