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Disease modeling in multiple sclerosis: Assessment and quantification of sources of variability in brain parenchymal fraction measurements.

, , , , , and . NeuroImage, 52 (4): 1367-1373 (2010)

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The effect of intramuscular interferon beta-1a on spinal cord volume in relapsing-remitting multiple sclerosis., , , , , , and . BMC Medical Imaging, (2016)Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis., , , , , , , , , and 33 other author(s). npj Digit. Medicine, (2019)Addressing Human Subjectivity via Transfer Learning: An Application to Predicting Disease Outcome in Multiple Sclerosis Patients., , , and . SDM, page 965-973. SIAM, (2014)Disease modeling in multiple sclerosis: Assessment and quantification of sources of variability in brain parenchymal fraction measurements., , , , , and . NeuroImage, 52 (4): 1367-1373 (2010)3 T MRI relaxometry detects T2 prolongation in the cerebral normal-appearing white matter in multiple sclerosis., , , , , , , , , and 1 other author(s). NeuroImage, 46 (3): 633-641 (2009)Domain Induced Dirichlet Mixture of Gaussian Processes: An Application to Predicting Disease Progression in Multiple Sclerosis Patients., , , , and . ICDM, page 1129-1134. IEEE Computer Society, (2015)Using multiple imputation to efficiently correct cerebral MRI whole brain lesion and atrophy data in patients with multiple sclerosis., , , , , , , , and . NeuroImage, (2015)A modeling perspective for meteor burst communication., , and . WSC, page 1022-1031. ACM Press, (1989)