{"id":"W2775394473","doi":"10.1016/j.compmedimag.2019.101660","title":"Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation","year":2019,"lang":"en","type":"preprint","venue":"Computerized Medical Imaging and Graphics","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Partial volume; White matter; Image segmentation; Limiting; Machine learning; Magnetic resonance imaging; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000720726,0.001515165,0.0008949767,0.001087353,0.0004363596,0.001103124,0.001314089,0.001848726,0.002753634],"category_scores_gemma":[0.002504006,0.0008154243,0.0009070258,0.0008065496,0.0003444272,0.0008311504,0.001352491,0.001583833,0.001507464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007984792,"about_ca_system_score_gemma":0.0008808883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01318268,"about_ca_topic_score_gemma":0.0253901,"domain_scores_codex":[0.9995647,0.00009134765,0.00001696422,0.0001421977,0.00009311148,0.00009171724],"domain_scores_gemma":[0.9992943,0.0002127102,0.0000497997,0.0001870866,0.0001974992,0.00005854524],"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":[0.0007081059,0.0001863796,0.003525871,0.0001585485,0.0001862846,0.0003902403,0.0001382695,0.371733,0.03326887,0.006866543,0.0168769,0.565961],"study_design_scores_gemma":[0.000004893242,0.0000221874,0.0006725794,0.00001731154,0.00002014942,0.00005881029,0.00001473729,0.9890098,0.006620377,0.002511558,0.001040907,0.000006631236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1649435,0.002145018,0.8125399,0.0007172844,0.0002869448,0.0001063573,0.002891177,0.009217262,0.007152624],"genre_scores_gemma":[0.7427874,0.0008950639,0.2346783,0.0002074046,0.0001717869,0.0000839635,0.006352015,0.0008971615,0.01392692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01318268,"threshold_uncertainty_score":0.02621186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362707681164554,"score_gpt":0.2991535171374735,"score_spread":0.2855264403258279,"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."}}