{"id":"W4297549244","doi":"10.3389/fninf.2022.1006532","title":"Accurate segmentation of neonatal brain MRI with deep learning","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"European Commission; UK Research and Innovation","keywords":"Computer science; Segmentation; Human Connectome Project; Pipeline (software); Artificial intelligence; Deep learning; Machine learning; Transfer of learning; Ground truth; Magnetic resonance imaging; Relevance (law); Pattern recognition (psychology); Neuroscience; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001800319,0.0001026245,0.0002354139,0.0002156986,0.00008102755,0.000005707017,0.000096219,0.00002567899,0.00004628883],"category_scores_gemma":[0.00006761876,0.00009184169,0.00003860382,0.0003303027,0.00007700773,0.0001736737,0.00009877144,0.0003996221,0.000003073107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004354577,"about_ca_system_score_gemma":0.00004470544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002992831,"about_ca_topic_score_gemma":0.000001422492,"domain_scores_codex":[0.9990432,0.00005684987,0.0003359367,0.0001005592,0.0002808795,0.0001826038],"domain_scores_gemma":[0.9995601,0.00005208361,0.0001775447,0.0001368269,0.00002454294,0.00004894714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005885113,0.0005200291,0.2319379,0.001812518,0.0002491651,0.002104748,0.05516002,0.1760421,0.003330809,0.0007491625,0.02073602,0.5014724],"study_design_scores_gemma":[0.01626152,0.009003181,0.04521038,0.0001507989,0.0001771361,0.003616134,0.08929659,0.7390846,0.004210625,0.0007597831,0.09132824,0.0009010374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7999552,0.0004838522,0.190173,0.001481758,0.0008864001,0.001072917,0.00003623756,0.0001035478,0.005807065],"genre_scores_gemma":[0.9208427,0.00008093016,0.07558148,0.002027913,0.00002965328,0.00004994249,0.0002474869,0.00003548337,0.001104412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5630425,"threshold_uncertainty_score":0.3745196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007012570272441496,"score_gpt":0.227469877194281,"score_spread":0.2204573069218395,"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."}}