{"id":"W3203732647","doi":"10.1002/mrm.29013","title":"Recovering SWI‐filtered phase data using deep learning","year":2021,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Multiple Sclerosis Society","keywords":"Susceptibility weighted imaging; Deep learning; Artificial intelligence; Computer science; Quantitative susceptibility mapping; Phase (matter); Artificial neural network; Filter (signal processing); Orientation (vector space); Pattern recognition (psychology); Computer vision; Mathematics; Magnetic resonance imaging; Physics; 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":[0.0005456117,0.0008146441,0.0002995612,0.0004260212,0.0001640977,0.0004723317,0.0004529533,0.0005170585,0.00120591],"category_scores_gemma":[0.001720187,0.0003023914,0.0004083554,0.0003194654,0.0003231825,0.0005849827,0.0004649067,0.0007096519,0.0004821954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006205391,"about_ca_system_score_gemma":0.00101487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003321689,"about_ca_topic_score_gemma":0.004735672,"domain_scores_codex":[0.9998875,0.00001639003,0.00000604682,0.00002904354,0.00004372598,0.00001729192],"domain_scores_gemma":[0.9997141,0.0001104019,0.00005305386,0.00003274906,0.00007547589,0.0000142942],"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":[0.0004595911,0.0001814205,0.007378114,0.0002808026,0.0001749789,0.0003715399,0.0001274147,0.4170341,0.144037,0.00205629,0.002516492,0.4253823],"study_design_scores_gemma":[0.00001983707,0.0001456145,0.002217248,0.00002919083,0.00003436586,0.0001909188,0.00002175427,0.9369903,0.05597344,0.002521977,0.001837413,0.00001792183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.168491,0.0007716214,0.8267289,0.0003892014,0.00005176518,0.00007315112,0.0003491714,0.001479633,0.001665453],"genre_scores_gemma":[0.7485175,0.0006175254,0.2455901,0.000306713,0.00004432468,0.0001073326,0.0008936416,0.000162173,0.003760657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003321689,"threshold_uncertainty_score":0.006604671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09824707144552418,"score_gpt":0.4122323390385838,"score_spread":0.3139852675930596,"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."}}