{"id":"W2277756578","doi":"","title":"Genetic algorithm driven statistically deformed models for medical image segmentation","year":2006,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Initialization; Maxima and minima; Segmentation; Artificial intelligence; Image segmentation; Computer science; Genetic algorithm; Computer vision; Pixel; Pattern recognition (psychology); Deformation (meteorology); Algorithm; Mathematics; Machine learning; Physics","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":[0.0001835954,0.0001973554,0.0001983978,0.0001340141,0.0002291157,0.0001720919,0.0003644311,0.0001086629,0.0000721937],"category_scores_gemma":[0.00006354277,0.0001977912,0.00004519748,0.0001891703,0.0002347404,0.0004700661,0.0001428012,0.0001039754,0.00001614366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007588271,"about_ca_system_score_gemma":0.0003783102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008230999,"about_ca_topic_score_gemma":0.000009177209,"domain_scores_codex":[0.9977976,0.0001099513,0.0005571886,0.0005223401,0.0006914457,0.0003214642],"domain_scores_gemma":[0.9987206,0.0002792703,0.00015425,0.0001785657,0.0004489125,0.0002183835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001358033,0.0001638577,0.0002388804,0.00008391779,0.00002587448,0.00002689803,0.0002574812,0.007577999,0.001170352,0.01846435,0.009094123,0.9628827],"study_design_scores_gemma":[0.00063285,0.0001249796,0.01320464,0.00002416583,0.00001210758,0.00005786556,0.00002490002,0.8375772,0.0002760359,0.1478333,0.00003572405,0.0001961963],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001489235,0.0001767745,0.9963763,0.0006608751,0.0001559227,0.0006155932,0.0000397243,0.0002413401,0.0002441819],"genre_scores_gemma":[0.08840267,0.00008397608,0.9106717,0.0003452967,0.0000993026,0.0001630062,0.0001399852,0.00001244691,0.00008168291],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9626865,"threshold_uncertainty_score":0.806569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383763865580076,"score_gpt":0.2685476613573816,"score_spread":0.2547100227015809,"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."}}