activity
20212023
most citedRCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation

111 citations · 118 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 111 cited

RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation

Xiangyu Zhao, Zengxin Qi, Sheng Wang +4

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that ar…

eess.IV2022★ 1 cited

TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation

Xiangyu Zhao, Di Zang, Sheng Wang +10

Brain network analysis for traumatic brain injury (TBI) patients is critical for its consciousness level assessment and prognosis evaluation, which requires the segmentation of cer…

cs.AI2022★ 1 cited

Building a 3-Player Mahjong AI using Deep Reinforcement Learning

Xiangyu Zhao, Sean B. Holden

Mahjong is a popular multi-player imperfect-information game developed in China in the late 19th-century, with some very challenging features for AI research. Sanma, being a 3-play…

cs.CV2022★ 4 cited

Wavelet-Attention CNN for Image Classification

Zhao Xiangyu

The feature learning methods based on convolutional neural network (CNN) have successfully produced tremendous achievements in image classification tasks. However, the inherent noi…

cs.CV2021

Morphable Detector for Object Detection on Demand

Xiangyun Zhao, Xu Zou, Ying Wu

Many emerging applications of intelligent robots need to explore and understand new environments, where it is desirable to detect objects of novel classes on the fly with minimum o…

eess.IV2021★ 1 cited

Prior Attention Network for Multi-Lesion Segmentation in Medical Images

Xiangyu Zhao, Peng Zhang, Fan Song +5

The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on th…