activity
20192022
most citedMed3D: Transfer Learning for 3D Medical Image Analysis

342 citations · 478 across the 29 of their papers we have counts for

collaborators

31 papers

cs.CV202214 cited

DFTR: Depth-supervised Fusion Transformer for Salient Object Detection

Heqin Zhu, Xu Sun, Yuexiang Li +3

Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than…

eess.IV2021

Training Automatic View Planner for Cardiac MR Imaging via Self-Supervision by Spatial Relationship between Views

Dong Wei, Kai Ma, Yefeng Zheng

View planning for the acquisition of cardiac magnetic resonance imaging (CMR) requires acquaintance with the cardiac anatomy and remains a challenging task in clinical practice. Ex…

cs.CV20211 cited

Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint

Jiawei Chen, Yuexiang Li, Kai Ma +1

Convolutional neural network (CNN) have proven its success for semantic segmentation, which is a core task of emerging industrial applications such as autonomous driving. However,…

eess.IV2021

Residual Moment Loss for Medical Image Segmentation

Quanziang Wang, Renzhen Wang, Yuexiang Li +3

Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segme…

cs.CV202116 cited

Stabilized Medical Image Attacks

Gege Qi, Lijun Gong, Yibing Song +2

Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, a threat to these systems arises that adversarial attacks make…

eess.IV20201 cited

MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint

Xinpeng Xie, Jiawei Chen, Yuexiang Li +3

Domain shift between medical images from multicentres is still an open question for the community, which degrades the generalization performance of deep learning models. Generative…