{"id":"W4402631950","doi":"10.1007/978-3-031-71602-7_8","title":"A Hybrid Neuroevolutionary Approach to the Design of Convolutional Neural Networks for 2D and 3D Medical Image Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Segmentation; Image segmentation; Image (mathematics); Computer vision; Artificial neural network; Pattern recognition (psychology)","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":[0.0006769545,0.0001917921,0.0003021391,0.0002649328,0.0001049221,0.00005872202,0.0002664017,0.00007039269,0.000008178677],"category_scores_gemma":[0.0001282924,0.000126914,0.00009131188,0.0001808244,0.0006716949,0.00005880695,0.0001792024,0.0004052478,0.00000165651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006496429,"about_ca_system_score_gemma":0.0001973538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008725126,"about_ca_topic_score_gemma":9.474986e-7,"domain_scores_codex":[0.9982887,0.0000249202,0.0002852985,0.0005474454,0.00063612,0.0002175482],"domain_scores_gemma":[0.9989803,0.0004406166,0.00008080371,0.0002240541,0.0001556514,0.000118531],"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":[0.0002083565,0.00009208758,0.0001676568,0.0004280196,0.0001241365,0.0001086229,0.0004215525,0.6803285,0.0004450868,0.002431129,0.001336309,0.3139085],"study_design_scores_gemma":[0.0001849708,0.000162264,0.0001056857,0.0002297567,0.00009654378,0.0003555024,4.476988e-7,0.9947214,0.00003934491,0.003843049,0.0001491341,0.0001119261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001258549,0.001142332,0.9943117,0.003355792,0.0002882844,0.0005113278,0.00001043357,0.00002250948,0.0002318022],"genre_scores_gemma":[0.3926246,0.0001235199,0.5997515,0.00496914,0.001588797,0.0000595544,0.00009664413,0.00005428388,0.000731952],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3945602,"threshold_uncertainty_score":0.5175402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01979546232546793,"score_gpt":0.2720051224917118,"score_spread":0.2522096601662439,"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."}}