22 citations · 60 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
Superpixel-Guided Label Softening for Medical Image Segmentation
Hang Li, Dong Wei, Shilei Cao +3
Segmentation of objects of interest is one of the central tasks in medical image analysis, which is indispensable for quantitative analysis. When developing machine-learning based…
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification
Dong Wei, Shilei Cao, Kai Ma +1
Deep neural network-based medical image classifications often use "hard" labels for training, where the probability of the correct category is 1 and those of others are 0. However,…
LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation
Shuxin Wang, Shilei Cao, Dong Wei +5
We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images. The main idea is to treat one-shot segmentation as a classical atlas-ba…