{"id":"W2121141217","doi":"10.1109/tpami.2010.24","title":"Self-Validated Labeling of Markov Random Fields for Image Segmentation","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Alberta","keywords":"Cut; Markov random field; Initialization; Image segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Markov chain; Segmentation; Maxima and minima; Robustness (evolution); Connected-component labeling; Algorithm; Mathematics; Scale-space segmentation; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003219678,0.0009229198,0.00104407,0.001487936,0.0006115338,0.001037416,0.001544186,0.002061164,0.001551224],"category_scores_gemma":[0.009460702,0.0008134673,0.001078539,0.00105538,0.001976439,0.002375325,0.00133746,0.001508762,0.0005374708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001727756,"about_ca_system_score_gemma":0.001286588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002023288,"about_ca_topic_score_gemma":0.002727123,"domain_scores_codex":[0.9982231,0.0008619947,0.00007209774,0.0003496598,0.0004117106,0.00008134176],"domain_scores_gemma":[0.9952107,0.003103843,0.0005440709,0.000631891,0.0004043115,0.000105233],"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":[0.0001506369,0.0000479302,0.0006458946,0.0001457366,0.00005607692,0.00007415727,0.000193165,0.7796404,0.01232411,0.05261414,0.001579415,0.1525284],"study_design_scores_gemma":[0.000008367383,0.00001530581,0.00007124271,0.000008986347,0.000004698818,0.00002521187,0.000006579093,0.9819819,0.002604607,0.01452482,0.0007383401,0.000009917929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002386667,0.00005906775,0.9971132,0.00004586683,0.000005727049,0.0000176995,0.00001132321,0.0001716662,0.0001887205],"genre_scores_gemma":[0.1031058,0.000159945,0.8954189,0.0000902032,0.00003949746,0.000153601,0.0001548259,0.0002164116,0.0006608567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003219678,"threshold_uncertainty_score":0.0170275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362402175961134,"score_gpt":0.2978464465344445,"score_spread":0.2842224247748332,"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."}}