11 citations · 23 across the 8 of their papers we have counts for
14 papers
Contrast Phase Classification with a Generative Adversarial Network
Yucheng Tang, Ho Hin Lee, Yuchen Xu +10
Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context…
Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution
Vishwesh Nath, Kurt G. Schilling, Colin B. Hansen +10
Confocal histology provides an opportunity to establish intra-voxel fiber orientation distributions that can be used to quantitatively assess the biological relevance of diffusion…
Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection
Samuel W. Remedios, Zihao Wu, Camilo Bermudez +6
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to cont…
Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
Camilo Bermudez, Justin Blaber, Samuel W. Remedios +6
Generalizability is an important problem in deep neural networks, especially in the context of the variability of data acquisition in clinical magnetic resonance imaging (MRI). Rec…
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
Yuankai Huo, Zhoubing Xu, Yunxi Xiong +7
Detailed whole brain segmentation is an essential quantitative technique, which provides a non-invasive way of measuring brain regions from a structural magnetic resonance imaging…
Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury
Samuel Remedios, Snehashis Roy, Justin Blaber +6
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data. However, to preserve patient…