{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001676958,0.000844432,0.00160763,0.001379185,0.0004615845,0.001681204,0.001959369,0.002136841,0.002179763],"category_scores_gemma":[0.003912242,0.0008555138,0.001135477,0.001114162,0.0014354,0.002404687,0.001523157,0.001613613,0.0006706045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001543752,"about_ca_system_score_gemma":0.001161922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002479793,"about_ca_topic_score_gemma":0.00250086,"domain_scores_codex":[0.9990964,0.000276902,0.00003794135,0.0001898722,0.0003272401,0.00007154212],"domain_scores_gemma":[0.9982916,0.0009837179,0.0002125178,0.000198522,0.000215502,0.00009814711],"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":[0.0003213096,0.00009532141,0.0005502357,0.0002784949,0.00007545493,0.0001586371,0.0002011755,0.754643,0.04156384,0.03073186,0.001796549,0.1695842],"study_design_scores_gemma":[0.000004656765,0.00001844952,0.00005673058,0.000005623579,0.000005842262,0.00003516558,0.000005386756,0.9921307,0.003759814,0.003513911,0.0004552984,0.000008433164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004506314,0.0001180489,0.9944373,0.00009914931,0.00001273958,0.00001911886,0.000025357,0.0002512764,0.0005306283],"genre_scores_gemma":[0.1824413,0.0002834182,0.8121487,0.000203787,0.00009668391,0.0001109893,0.0002984511,0.0004723887,0.003944305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002479793,"threshold_uncertainty_score":0.01120073,"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."}}