{"id":"W1981665992","doi":"10.5244/c.24.101","title":"TV-Based Multi-Label Image Segmentation with Label Cost Prior","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Minimum description length; Segmentation; Image segmentation; Computer science; Regular polygon; Convex optimization; Regularization (linguistics); Pattern recognition (psychology); Artificial intelligence; Energy functional; Mathematical optimization; Mathematics; Algorithm","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":[0.0003083786,0.0001543546,0.0001243132,0.0001091832,0.0001149749,0.0002445333,0.0006010269,0.00006505214,0.0004077406],"category_scores_gemma":[0.00007998157,0.0001167756,0.00001928723,0.000327131,0.0001402829,0.0008509707,0.00008817024,0.0002431344,0.0001566843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003397134,"about_ca_system_score_gemma":0.0001292323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006828686,"about_ca_topic_score_gemma":0.00009480784,"domain_scores_codex":[0.9986376,0.00004871703,0.0002285853,0.0003845287,0.0004464571,0.000254171],"domain_scores_gemma":[0.9989052,0.00009658937,0.0001083905,0.0005108218,0.0001913922,0.0001876281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001028377,0.0004168972,0.0003936769,0.00001938266,0.000009214437,0.00002263546,0.000171349,7.50615e-7,0.5947031,0.001047865,0.003592842,0.3996119],"study_design_scores_gemma":[0.002604207,0.0001543733,0.0007154848,0.00001512429,0.000007041263,0.000008620328,0.00002863747,0.187981,0.8080307,0.00005955294,0.0001674719,0.0002277907],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005771033,0.000003055122,0.9906638,0.001024461,0.0001306017,0.0006196123,0.000003130333,0.0007674005,0.001016867],"genre_scores_gemma":[0.009249853,0.000001653299,0.9867464,0.002977267,0.00002553935,0.0001226439,0.00001455094,0.00001445908,0.0008476102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3993841,"threshold_uncertainty_score":0.476197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02474284707216573,"score_gpt":0.3203234121387215,"score_spread":0.2955805650665557,"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."}}