{"id":"W1599513353","doi":"10.1007/11566465_47","title":"Segmentation of Focal Cortical Dysplasia Lesions Using a Feature-Based Level Set","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Cortical dysplasia; Computer science; Segmentation; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Set (abstract data type); Epilepsy; Cortex (anatomy); Level set (data structures); Similarity (geometry); Magnetic resonance imaging; Medicine; Radiology; Neuroscience; Image (mathematics); 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":[0.0003293754,0.0001708556,0.0001817803,0.000262081,0.0002503461,0.00009836163,0.001203378,0.00007159974,0.000002409284],"category_scores_gemma":[0.00008502564,0.0001573878,0.00004919328,0.002220659,0.0003759774,0.0004822019,0.0003365747,0.0002773298,0.000004896208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001666018,"about_ca_system_score_gemma":0.0002822675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008663967,"about_ca_topic_score_gemma":0.00004874505,"domain_scores_codex":[0.9980647,0.00006843018,0.0002893494,0.0006449671,0.0005165064,0.0004160333],"domain_scores_gemma":[0.998547,0.0004172492,0.0001329423,0.000651804,0.000130212,0.0001208515],"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":[0.000004889755,0.0000565681,0.0007225908,0.000004708838,0.000001381173,0.000002669332,0.0001982593,0.7462009,0.0316323,0.0009044776,0.000008712032,0.2202625],"study_design_scores_gemma":[0.0002602874,0.0000646004,0.003399109,0.00003242624,0.000002862386,0.00002604839,3.627223e-7,0.9051421,0.08873811,0.002156085,0.0000272692,0.0001507099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0631398,0.00003999176,0.9345767,0.001727067,0.0001923572,0.0002513292,0.000003541717,0.00006534103,0.000003831284],"genre_scores_gemma":[0.5072182,8.736349e-7,0.4921764,0.000530143,0.00006286985,0.000005824737,0.000001275361,0.000004104874,2.666441e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4440785,"threshold_uncertainty_score":0.6418087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04695083398903657,"score_gpt":0.3218166831586094,"score_spread":0.2748658491695728,"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."}}