{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006588324,0.0005597739,0.000581436,0.0007257166,0.000343788,0.000644048,0.001069633,0.001318143,0.001155635],"category_scores_gemma":[0.002500522,0.0004348674,0.0006389045,0.0006906708,0.001016834,0.0006907752,0.000720563,0.0009194145,0.0002838084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001163449,"about_ca_system_score_gemma":0.001037573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003629999,"about_ca_topic_score_gemma":0.003267178,"domain_scores_codex":[0.9997264,0.0000966393,0.00001062622,0.00004444278,0.0001041781,0.00001774458],"domain_scores_gemma":[0.9994867,0.0003032892,0.00006006205,0.00005680896,0.00007152632,0.00002168007],"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":[0.000006754851,0.000008077747,0.0001351996,0.000008407526,0.00001090754,0.00002374626,0.00001766587,0.9782736,0.001424327,0.01094064,0.0001683805,0.008982272],"study_design_scores_gemma":[0.000001566952,0.000004202821,0.00002013287,0.000001371386,0.000001264807,0.000006482848,0.000001585252,0.9954732,0.0002004919,0.004056397,0.0002310596,0.000002298903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00837517,0.0001102774,0.9898666,0.0001818554,0.00001716741,0.00002250454,0.00002022778,0.0001754511,0.001230804],"genre_scores_gemma":[0.4437973,0.0003779402,0.5513053,0.0002096955,0.00003996844,0.0003391594,0.0001586301,0.0001666874,0.003605352],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003629999,"threshold_uncertainty_score":0.008441508,"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."}}