21 citations · 27 across the 6 of their papers we have counts for
9 papers
Learning Shape Priors by Pairwise Comparison for Robust Semantic Segmentation
Cong Xie, Hualuo Liu, Shilei Cao +4
Semantic segmentation is important in medical image analysis. Inspired by the strong ability of traditional image analysis techniques in capturing shape priors and inter-subject si…
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network
Shuxin Wang, Shilei Cao, Zhizhong Chai +4
Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for examp…
RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation
Cong Xie, Shilei Cao, Dong Wei +6
Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance. Consider…
Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation
Hong-Yu Zhou, Hualuo Liu, Shilei Cao +5
Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given sever…
Brain Atlas Guided Attention U-Net for White Matter Hyperintensity Segmentation
Zicong Zhang, Kimerly Powell, Changchang Yin +4
White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important t…
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
Zifeng Wang, Rui Wen, Xi Chen +4
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main chall…