{"id":"W2431976432","doi":"10.1049/iet-ipr.2016.0271","title":"Non‐local‐based spatially constrained hierarchical fuzzy <i>C</i> ‐means method for brain magnetic resonance imaging segmentation","year":2016,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Jiangsu Province; National Research Foundation of Korea","keywords":"Segmentation; Magnetic resonance imaging; Fuzzy logic; Image segmentation; Computer science; Artificial intelligence; Computer vision; Nuclear magnetic resonance; Physics; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001330575,0.0002965189,0.0002972365,0.0002019597,0.0002669278,0.0004334191,0.0008031761,0.00007893286,0.00004802138],"category_scores_gemma":[0.0004859472,0.0002359909,0.000107854,0.0004436908,0.0004361836,0.0014934,0.0001366501,0.0001724505,0.00001626752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001278656,"about_ca_system_score_gemma":0.0004747721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001554917,"about_ca_topic_score_gemma":0.000005196102,"domain_scores_codex":[0.9971962,0.0001991353,0.0006067486,0.0008160555,0.0005881495,0.0005937138],"domain_scores_gemma":[0.9979308,0.0007822993,0.0002537886,0.0004367484,0.0003661459,0.0002302308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002254564,0.00004374877,0.00004906969,0.00007711491,0.00000168291,0.0000150892,0.0002137712,0.000002609775,0.269767,0.0001425492,0.00136681,0.728298],"study_design_scores_gemma":[0.003002312,0.0002129419,0.0002250283,0.0005434302,0.00002113573,0.00003661961,0.00006302631,0.3839636,0.60123,0.009168848,0.0009993427,0.000533712],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005099652,0.0002730273,0.9854352,0.0121668,0.0001226645,0.0007474777,0.00002450373,0.0005840623,0.0005952361],"genre_scores_gemma":[0.02063667,0.00000594148,0.972013,0.006692541,0.0001036012,0.0002750154,0.00001314119,0.0000404291,0.0002196457],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7277643,"threshold_uncertainty_score":0.9623428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169007938882085,"score_gpt":0.3085751235614408,"score_spread":0.29688504417262,"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."}}