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Myelin water imaging of multiple sclerosis at 7 T: Correlations with histopathology.

, , , , , and . NeuroImage, 40 (4): 1575-1580 (2008)

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Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation., , , , and . MLMI, volume 8679 of Lecture Notes in Computer Science, page 117-124. Springer, (2014)Hierarchical Multimodal Fusion of Deep-Learned Lesion and Tissue Integrity Features in Brain MRIs for Distinguishing Neuromyelitis Optica from Multiple Sclerosis., , , , , , , , and . MICCAI (3), volume 10435 of Lecture Notes in Computer Science, page 480-488. Springer, (2017)Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation., , , , , and . MICCAI (3), volume 9351 of Lecture Notes in Computer Science, page 3-11. Springer, (2015)Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis., , , , , , and . LABELS/DLMIA@MICCAI, volume 10008 of Lecture Notes in Computer Science, page 86-94. (2016)Myelin water imaging of multiple sclerosis at 7 T: Correlations with histopathology., , , , , and . NeuroImage, 40 (4): 1575-1580 (2008)Corpus Callosum Segmentation in Brain MRIs via Robust Target-Localization and Joint Supervised Feature Extraction and Prediction., , , , , , and . MICCAI (2), volume 9901 of Lecture Notes in Computer Science, page 406-414. (2016)Modeling the Variability in Brain Morphology and Lesion Distribution in Multiple Sclerosis by Deep Learning., , , , and . MICCAI (2), volume 8674 of Lecture Notes in Computer Science, page 462-469. Springer, (2014)