{"id":"W3038503126","doi":"10.1007/s12021-020-09475-7","title":"DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data","year":2020,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; KWF Kankerbestrijding","keywords":"Computer science; Convolutional neural network; Data curation; Artificial intelligence; Pattern recognition (psychology); Magnetic resonance imaging; Contrast (vision); Sorting; Metadata; sort; Data mining; Information retrieval; Algorithm; Radiology; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001524855,0.002277608,0.001614397,0.005393081,0.001058636,0.002090738,0.003407031,0.001240977,0.01048782],"category_scores_gemma":[0.003739055,0.00124575,0.001782348,0.003422478,0.0005858517,0.002753237,0.002776539,0.001324343,0.004628955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001561652,"about_ca_system_score_gemma":0.003549908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0110323,"about_ca_topic_score_gemma":0.01974705,"domain_scores_codex":[0.9989529,0.00006994935,0.0001568736,0.0003480939,0.0003568763,0.000115392],"domain_scores_gemma":[0.9987734,0.000291856,0.0001339331,0.0003173033,0.0003935503,0.00009003033],"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":[0.0007010028,0.0001607178,0.003503097,0.0003685051,0.0002392486,0.0002453877,0.0001338567,0.01046977,0.02278753,0.003810076,0.04971188,0.9078688],"study_design_scores_gemma":[0.0003729818,0.0003390464,0.007253747,0.000141294,0.0001087441,0.0008789055,0.0002821188,0.7763508,0.1026907,0.02797386,0.08339026,0.0002174712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03553789,0.001675437,0.863828,0.0004715447,0.0005356526,0.0007177654,0.009035024,0.0853602,0.002838411],"genre_scores_gemma":[0.06790911,0.0004993987,0.8989167,0.0003671988,0.00008713338,0.0005687936,0.02333461,0.002849365,0.005467747],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0110323,"threshold_uncertainty_score":0.03508526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03043432275730318,"score_gpt":0.3135303376829426,"score_spread":0.2830960149256394,"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."}}