{"id":"W2942354470","doi":"10.1002/mrm.27772","title":"Conditional generative adversarial network for 3D rigid‐body motion correction in MRI","year":2019,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid","keywords":"Artificial intelligence; Computer science; Ground truth; Computer vision; Discriminator; Image quality; Image (mathematics); Motion (physics); Artifact (error); Pattern recognition (psychology); Mathematics","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":[0.0009677879,0.000792331,0.0004799275,0.0003389891,0.0002130435,0.0003817431,0.0008626739,0.00081356,0.001434743],"category_scores_gemma":[0.001934057,0.0004199544,0.0006929147,0.00024456,0.0007247705,0.0004323046,0.0008415972,0.001512366,0.0003049358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007864947,"about_ca_system_score_gemma":0.0005581104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005511696,"about_ca_topic_score_gemma":0.004388922,"domain_scores_codex":[0.999749,0.0001044381,0.000008687952,0.00005134299,0.00005835076,0.00002807427],"domain_scores_gemma":[0.9994362,0.0003704864,0.00006708546,0.00004105085,0.00006095973,0.00002421155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004166612,0.00001110876,0.0002613328,0.00001444375,0.00001949595,0.0000345503,0.00001465264,0.9819759,0.001528394,0.001162015,0.00034337,0.01459292],"study_design_scores_gemma":[0.00000122741,0.000007117349,0.00004556817,0.000001893663,0.000002159406,0.000006701043,9.891112e-7,0.9989694,0.0003809904,0.0005043095,0.00007777695,0.000001802708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03309001,0.0005454304,0.9634769,0.0003925107,0.00005331826,0.00005736407,0.00009426219,0.0007973772,0.001492865],"genre_scores_gemma":[0.8705821,0.0004440974,0.1227917,0.0003585072,0.0000529791,0.0001635373,0.0003336677,0.0001884671,0.005084987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005511696,"threshold_uncertainty_score":0.01095927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01006326330246969,"score_gpt":0.2435687284096342,"score_spread":0.2335054651071645,"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."}}