{"id":"W4388589244","doi":"10.1093/neuonc/noad179.0780","title":"NIMG-85. EVALUATING THE PERFORMANCE OF A DEEP LEARNING GLIOMA SEGMENTATION MODEL USING COMBINED PRE- AND POST-TREATMENT MRI TRAINING DATA","year":2023,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health Services","funders":"","keywords":"Computer science; Segmentation; Convolutional neural network; Workflow; Magnetic resonance imaging; Artificial intelligence; Glioma; Dice; Deep learning; Pattern recognition (psychology); Machine learning; Medicine; Radiology","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":[0.004455817,0.002419993,0.001034926,0.001227612,0.0005304685,0.001011898,0.002084656,0.002948151,0.001952913],"category_scores_gemma":[0.005637197,0.0007633905,0.00182057,0.000843795,0.0006786987,0.0007725091,0.0009189091,0.001500921,0.001541984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002498908,"about_ca_system_score_gemma":0.002401177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04768748,"about_ca_topic_score_gemma":0.03377549,"domain_scores_codex":[0.9991124,0.0002942845,0.00005341155,0.0002777247,0.0001416813,0.0001204083],"domain_scores_gemma":[0.9985228,0.0006425727,0.0001241416,0.0001901042,0.0003635614,0.0001568968],"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":[0.002142003,0.001192138,0.02109334,0.0006122509,0.00138719,0.0003919336,0.0001194929,0.7779008,0.006903922,0.0006190379,0.02948218,0.1581557],"study_design_scores_gemma":[0.0001302773,0.0005816522,0.004493426,0.00005009946,0.0001272243,0.00006922322,0.00003249853,0.9872951,0.005031757,0.0004178889,0.001740991,0.00002975673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9082156,0.006267705,0.03587734,0.002135854,0.0009492123,0.0008435113,0.01634665,0.02074362,0.008620545],"genre_scores_gemma":[0.9159527,0.0007126445,0.03984056,0.001072369,0.0001089279,0.0004715778,0.03619587,0.0005705129,0.005074746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04768748,"threshold_uncertainty_score":0.09481978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07624121402978769,"score_gpt":0.3977261482719263,"score_spread":0.3214849342421386,"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."}}