{"id":"W4417297438","doi":"10.1109/tmi.2025.3638977","title":"BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Toronto","funders":"National Institutes of Health; Thrasher Research Fund","keywords":"Segmentation; Magnetic resonance imaging; Hypoxic Ischemic Encephalopathy; Lesion; Diffusion MRI; Image segmentation","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":[0.006534462,0.003459218,0.001645708,0.00344866,0.00144793,0.002847776,0.004115013,0.003856129,0.006037924],"category_scores_gemma":[0.01924989,0.0006970061,0.002187431,0.00232921,0.0009279078,0.001572879,0.003988384,0.00225818,0.007824143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003061399,"about_ca_system_score_gemma":0.003174211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0311061,"about_ca_topic_score_gemma":0.03799113,"domain_scores_codex":[0.9960781,0.001021605,0.0004298407,0.001019622,0.001070228,0.0003806882],"domain_scores_gemma":[0.9944986,0.001607913,0.0003010136,0.001269942,0.001868875,0.0004537696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002181568,0.0005879176,0.01402394,0.003157449,0.0007706014,0.001131178,0.0003954873,0.04167608,0.01042815,0.00436353,0.7736627,0.1476214],"study_design_scores_gemma":[0.001136335,0.001320923,0.04575082,0.00201826,0.000401151,0.003830583,0.0009702186,0.2733582,0.05064925,0.01548765,0.6046175,0.0004591138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1768139,0.01659266,0.07334652,0.009058042,0.003907845,0.001974055,0.6219882,0.07804605,0.01827266],"genre_scores_gemma":[0.08092776,0.001280424,0.05381265,0.00105246,0.0002181393,0.0008309049,0.8539789,0.003426756,0.004471988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0311061,"threshold_uncertainty_score":0.06185007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02753541641764816,"score_gpt":0.3389714132744883,"score_spread":0.3114359968568401,"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."}}