{"id":"W2125593607","doi":"10.1109/icip.2004.1421666","title":"Image partioning by level set multiregion competition","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image segmentation; Context (archaeology); Segmentation; Competition (biology); Algorithm; Computer science; Image (mathematics); Domain (mathematical analysis); Level set (data structures); Representation (politics); Set (abstract data type); Partition (number theory); Mathematics; Minification; Artificial intelligence; Theoretical computer science; Mathematical optimization; Pattern recognition (psychology); Combinatorics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001763288,0.0005362644,0.001033626,0.0006681685,0.000515276,0.001117948,0.001659008,0.0009395951,0.002230354],"category_scores_gemma":[0.002825723,0.0004702678,0.0008428556,0.0008848234,0.001105011,0.001444204,0.001988413,0.001057631,0.0005081948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141625,"about_ca_system_score_gemma":0.0008518947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003173111,"about_ca_topic_score_gemma":0.002843818,"domain_scores_codex":[0.9991774,0.0002187792,0.00002805402,0.0001336274,0.0003514986,0.00009061331],"domain_scores_gemma":[0.9989875,0.000474683,0.00008153224,0.0001890846,0.0001714926,0.00009560426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002687046,0.00007250012,0.0006748554,0.0001254844,0.00005742642,0.0001440443,0.0002758734,0.7448874,0.05394748,0.06540743,0.001678378,0.1324605],"study_design_scores_gemma":[0.000007709645,0.00002898092,0.00009692649,0.000003642022,0.000004611698,0.0000405649,0.000009434008,0.9879996,0.004118565,0.00666817,0.001014457,0.000007149594],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01359019,0.00009088631,0.9846659,0.0000754349,0.00001138818,0.00004084415,0.00001506922,0.0001657791,0.001344573],"genre_scores_gemma":[0.4688469,0.0001797129,0.5251354,0.00020319,0.00003456688,0.000187601,0.0001637345,0.0002899372,0.00495894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003173111,"threshold_uncertainty_score":0.009325266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04014240524596725,"score_gpt":0.3122583602445898,"score_spread":0.2721159549986226,"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."}}