{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007871473,0.0002023165,0.0003899875,0.000160147,0.00008530683,0.0000335755,0.0003350675,0.0001076249,0.0002726895],"category_scores_gemma":[0.0001831636,0.00017869,0.00004789541,0.0006368104,0.0001113577,0.0003226651,0.00006379472,0.000206678,0.00003026382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001259501,"about_ca_system_score_gemma":0.00006682078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002121581,"about_ca_topic_score_gemma":0.0002398494,"domain_scores_codex":[0.9980106,0.0002018478,0.0004622937,0.0005771652,0.0003265771,0.0004214526],"domain_scores_gemma":[0.9989604,0.0004189221,0.0001213579,0.0003225911,0.0001156182,0.00006111729],"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":[0.0005498009,0.0003048659,0.03940836,0.00005298456,0.000020983,0.00005383181,0.002413769,0.3537522,0.002399737,0.02107621,0.1136606,0.4663066],"study_design_scores_gemma":[0.002777683,0.0006515056,0.07206635,0.0001546652,0.000006536293,0.000005852827,0.00005231619,0.8801867,0.0001246789,0.00485345,0.0389209,0.0001993253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007264922,0.002458972,0.9789005,0.002671943,0.005371614,0.001169818,0.000006515842,0.00004110373,0.002114645],"genre_scores_gemma":[0.8415249,0.0004331346,0.1487885,0.001692933,0.003648915,0.0003517391,0.000113131,0.00003229054,0.003414489],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.83426,"threshold_uncertainty_score":0.7286766,"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."}}