1 citations · 1 across the 5 of their papers we have counts for
6 papers
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images
Xiaowe Xu, Jiawei Zhang, Jinglan Liu +10
As one of the most commonly ordered imaging tests, computed tomography (CT) scan comes with inevitable radiation exposure that increases the cancer risk to patients. However, CT im…
ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease
Xiaowei Xu, Tianchen Wang, Jian Zhuang +6
Congenital heart disease (CHD) is the most common type of birth defect, which occurs 1 in every 110 births in the United States. CHD usually comes with severe variations in heart s…
Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective
Shao-Cheng Wen, Yu-Jen Chen, Zihao Liu +7
Deep learning had already demonstrated its power in medical images, including denoising, classification, segmentation, etc. All these applications are proposed to automatically ana…
ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation
Tianchen Wang, Xiaowei Xu, Jinjun Xiong +5
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions. In order to enable fast and accurate visual assistance, the t…
Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising
Jinglan Liu, Yukun Ding, Jinjun Xiong +6
CT image denoising can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain (noisy images) and a target domain …
Accurate Congenital Heart Disease Model Generation for 3D Printing
Xiaowei Xu, Tianchen Wang, Dewen Zeng +5
3D printing has been widely adopted for clinical decision making and interventional planning of Congenital heart disease (CHD), while whole heart and great vessel segmentation is t…