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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)

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Predicting Catheter Ablation Outcomes with Pre-ablation Heart Rhythm Data: Less Is More., , , , , and . MLMI@MICCAI, volume 12436 of Lecture Notes in Computer Science, page 563-571. Springer, (2020)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)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)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)Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections., , , , , , , , and . DLMIA/ML-CDS@MICCAI, volume 10553 of Lecture Notes in Computer Science, page 330-337. Springer, (2017)