{"id":"W3111988545","doi":"10.1093/neuonc/noaa222.357","title":"IMG-22. A DEEP LEARNING MODEL FOR AUTOMATIC POSTERIOR FOSSA PEDIATRIC BRAIN TUMOR SEGMENTATION: A MULTI-INSTITUTIONAL STUDY","year":2020,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Segmentation; Deep learning; Computer science; Ground truth; Artificial intelligence; Medulloblastoma; Ependymoma; Pilocytic astrocytoma; Convolutional neural network; Neuroradiologist; Medicine; Magnetic resonance imaging; Radiology; Astrocytoma; Glioma; Pathology","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":[0.002870422,0.001151643,0.0005588756,0.001024599,0.000286095,0.000735807,0.001307244,0.0009852706,0.001114213],"category_scores_gemma":[0.005959752,0.0003855904,0.0008190466,0.000769356,0.0005096613,0.0007466542,0.0008160752,0.0009504058,0.000519471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109306,"about_ca_system_score_gemma":0.0007513851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01118982,"about_ca_topic_score_gemma":0.00956978,"domain_scores_codex":[0.9991736,0.0003338888,0.00005433271,0.0002189959,0.0001320169,0.00008724076],"domain_scores_gemma":[0.9971733,0.001118899,0.0003584912,0.0005855053,0.0005611836,0.0002025352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002666425,0.00229773,0.3755405,0.0004379332,0.001835062,0.001639548,0.0004059694,0.337171,0.01036049,0.001172254,0.0147722,0.251701],"study_design_scores_gemma":[0.0001639883,0.00161495,0.06851865,0.00005266284,0.0003633882,0.001053235,0.0002562459,0.910778,0.01291523,0.0007860443,0.003426385,0.00007124955],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745335,0.0004834189,0.02115861,0.0001790466,0.00003412162,0.0001430158,0.002175769,0.0006879246,0.0006046138],"genre_scores_gemma":[0.9767728,0.0001887732,0.01704486,0.00009014351,0.00002358545,0.0001501434,0.005012461,0.0001463165,0.0005709325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01118982,"threshold_uncertainty_score":0.0222494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04452866306211238,"score_gpt":0.3333100915936235,"score_spread":0.2887814285315111,"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."}}