{"id":"W4398836484","doi":"10.48550/arxiv.2405.14590","title":"MAMOC: MRI Motion Correction via Masked Autoencoding","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Eisai; Alzheimer's Association","keywords":"Motion (physics); Computer science; Computer vision","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":[0.001163736,0.001345573,0.000660732,0.0008169736,0.0003933903,0.0009033052,0.001699539,0.001062511,0.002808037],"category_scores_gemma":[0.00632644,0.0004524353,0.0007548239,0.0005567009,0.0005697916,0.00105392,0.001750474,0.001298727,0.001620311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003951952,"about_ca_system_score_gemma":0.001628611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00446561,"about_ca_topic_score_gemma":0.00816825,"domain_scores_codex":[0.9994575,0.0001306495,0.00003927706,0.0001585569,0.0001628495,0.00005107105],"domain_scores_gemma":[0.9985806,0.0003872866,0.0001817667,0.0005133075,0.0002480928,0.00008902367],"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":[0.0009603957,0.0002358643,0.003453743,0.0005522939,0.0003418984,0.0004052809,0.0001967543,0.06810047,0.06482773,0.004266301,0.03535761,0.8213017],"study_design_scores_gemma":[0.0001686068,0.0006447794,0.003538761,0.0001234942,0.0001434746,0.002019489,0.00008965386,0.8344063,0.1116198,0.01142897,0.03569678,0.0001198704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04408447,0.002352076,0.9266387,0.000549095,0.000520399,0.0003483064,0.001691837,0.02181472,0.002000393],"genre_scores_gemma":[0.2739757,0.001173043,0.7072504,0.0007900367,0.0002941443,0.000336864,0.007296747,0.001956261,0.006926781],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00446561,"threshold_uncertainty_score":0.009393811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06053273508991173,"score_gpt":0.2289670374937464,"score_spread":0.1684343024038347,"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."}}