Law MT, Traboulsee AL, Li DK, Carruthers RL, Freedman MS, Kolind SH, Tam R. Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression. Mult Scler J Exp Transl Clin. 2019 Nov 6;5(4):2055217319885983.
Yoo Y, Tang LYW, Brosch T, Li DKB, Kolind S, Vavasour I, Rauscher A, MacKay AL, Traboulsee A, Tam RC. 2017. Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. Neuroimage Clin. Oct 14;17:169-178.
Yoo Y, Tang LYW, Kim S, Kim HJ, Lee LE, Li DKB, Kolind S, Traboulsee A, R. Tam. 2017. Hierarchical multimodal fusion of deep-learned lesion and tissue integrity features in brain MRIs for distinguishing neuromyelitis optica from multiple sclerosis. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Part III, pages 480–488.
Brosch T, Yoo Y, Tang LYW, Tam R. 2016 Deep learning of brain images and its application to multiple sclerosis. In G. Wu, D. Shen, and M. Sabuncu, editors, Machine Learning and Medical Imaging, chapter 3.
Tang LYW, Brosch T, Liu X, Traboulsee A, Li DKB, Tam R. 2016. Corpus callosum segmentation in brain MRIs via robust target-localization and joint supervised feature extraction and prediction. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Part II, pages 406–414.
Yoo Y, Tang LYW, Brosch T, Li DKB, Metz L, Traboulsee T, Tam R. 2016. Deep learning of brain lesion patterns for predicting future disease activity in patients with early symptoms of multiple sclerosis. In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Deep Learning in Medical Image Analysis (DLMIA), pages 86–94.
Brosch T, Tang LY, Yoo Y, Li DK, Traboulsee A, Tam R. 2016. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. IEEE Trans Med Imaging. 2016 May;35(5):1229-1239.
T. Brosch and R. Tam. Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D images.Neural Computation, 27(1):211–227, 2015.
Brosch T, Yoo Y, Li DK, Traboulsee A, Tam R. 2014. Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning. Med Image Comput Comput Assist Interv. 2014;17(Pt 2):462-9.
Brosch T, Tam R; Initiative for the Alzheimers Disease Neuroimaging. 2013. Manifold learning of brain MRIs by deep learning. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):633-40.