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Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks., , , , , , , , and . BMC Medical Imaging, 21 (1): 69 (2021)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)Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation., , , , , and . IEEE Trans. Medical Imaging, 35 (5): 1229-1239 (2016)Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks., , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 11314 of SPIE Proceedings, SPIE, (2020)Manifold Learning of Brain MRIs by Deep Learning., and . MICCAI (2), volume 8150 of Lecture Notes in Computer Science, page 633-640. Springer, (2013)Spine centerline extraction and efficient spine reading of MRI and CT data., , , and . Medical Imaging: Image Processing, volume 10574 of SPIE Proceedings, page 1057425. SPIE, (2018)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)Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease., , , , , , , and . Medical Image Anal., (2022)Efficient Training of Convolutional Deep Belief Networks in the Frequency Domain for Application to High-Resolution 2D and 3D Images., and . Neural Comput., 27 (1): 211-227 (2015)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)