Lin Shi

TL;DR: This paper proposes a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN), outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin.

TL;DR: A novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs) that is fast and accurate, demonstrating a good potential in clinical routines.

TL;DR: A novel joint learning model with CNN J-CNN that can effectively identify the type of vertebra and eliminate false detections based on a set of coarse vertebral centroids generated from a random forest classifier.

TL;DR: Following a Bayesian modeling approach, a generalized total variation-based MRI denoising model is proposed based on global hyper-Laplacian prior and Rician noise assumption and has the properties of backward diffusion in local normal directions and forward diffusion inLocal tangent directions.

TL;DR: A strategic network involving several overlapping and domain-specific cortical and subcortical structures was identified for each of the cognitive domains and two assumption-free analyses consistently identified the left angular gyrus, left basal ganglia structures and the white matter around the left basal Ganglia as strategic structures for global cognitive impairment after stroke.