{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001793873,0.00006980816,0.00006173914,0.00004157975,0.00007714311,0.0001164845,0.000279671,0.00003078956,0.0002128645],"category_scores_gemma":[0.00003299921,0.00006496597,0.0000244914,0.0001200818,0.00004383613,0.0006679036,0.00008056914,0.00006553077,0.0002437661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003842797,"about_ca_system_score_gemma":0.00001383869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002811194,"about_ca_topic_score_gemma":0.00000879809,"domain_scores_codex":[0.9992151,0.00004512691,0.0001623118,0.0002039006,0.0002302139,0.0001433383],"domain_scores_gemma":[0.9995655,0.00003124766,0.00004827728,0.0002198871,0.00005260274,0.00008250291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002641489,0.0001253718,0.000077497,0.00001049284,0.000007143371,0.000003866106,0.0005407942,0.00000631765,0.1659518,0.01590908,0.4280502,0.3893149],"study_design_scores_gemma":[0.0005843834,0.00006687525,0.000443729,0.00002802875,0.000002995352,0.00001648913,0.00005861236,0.1807886,0.7951999,0.0005215521,0.02201811,0.0002708136],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007292298,0.00001383256,0.9926792,0.003188726,0.00004449713,0.00009657456,0.000003283805,0.0004503896,0.002794286],"genre_scores_gemma":[0.1105907,0.00001301304,0.8850245,0.002620527,0.00005167866,0.00001983808,0.00002773321,0.000004954843,0.001647044],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6292481,"threshold_uncertainty_score":0.3133202,"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."}}