53 citations · 61 across the 4 of their papers we have counts for
6 papers
Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks
Zongyuan Ge, Xin Wang
The current generation of deep neural networks has achieved close-to-human results on "closed-set" image recognition; that is, the classes being evaluated overlap with the training…
Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization
Wei Feng, Lie Ju, Lin Wang +5
Retinal vessel segmentation plays a key role in computer-aided screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Recently, deep learning-based…
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition
Lie Ju, Xin Wang, Lin Wang +5
In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples), which r…
Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling
Lie Ju, Xin Wang, Xin Zhao +3
Recently, ultra-widefield (UWF) 200\degree~fundus imaging by Optos cameras has gradually been introduced because of its broader insights for detecting more information on the fundu…
Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation
Lie Ju, Xin Wang, Quan Zhou +5
For decades, advances in retinal imaging technology have enabled effective diagnosis and management of retinal disease using fundus cameras. Recently, ultra-wide-field (UWF) fundus…
Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning
Lie Ju, Xin Wang, Xin Zhao +4
The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and…