most citedSelf-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation

11 citations · 20 across the 4 of their papers we have counts for

collaborators

6 papers

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…

cs.CV20204 cited

Instance-aware Self-supervised Learning for Nuclei Segmentation

Xinpeng Xie, Jiawei Chen, Yuexiang Li +3

Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology.…

eess.IV202011 cited

Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation

Yuexiang Li, Jiawei Chen, Xinpeng Xie +2

Witnessing the success of deep learning neural networks in natural image processing, an increasing number of studies have been proposed to develop deep-learning-based frameworks fo…

cs.CV20184 cited

Texture Deformation Based Generative Adversarial Networks for Face Editing

WenTing Chen, Xinpeng Xie, Xi Jia +1

Despite the significant success in image-to-image translation and latent representation based facial attribute editing and expression synthesis, the existing approaches still have…

cs.CV2018

Reversed Active Learning based Atrous DenseNet for Pathological Image Classification

Yuexiang Li, Xinpeng Xie, Linlin Shen +1

Witnessed the development of deep learning in recent years, increasing number of researches try to adopt deep learning model for medical image analysis. However, the usage of deep…

cs.CV2018

Active Learning for Breast Cancer Identification

Xinpeng Xie, Yuexiang Li, Linlin Shen

Breast cancer is the second most common malignancy among women and has become a major public health problem in current society. Traditional breast cancer identification requires ex…