activity
20202022
most citedLeveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling

53 citations · 120 across the 12 of their papers we have counts for

collaborators

12 papers

eess.IV2022★ 2 cited

3D Matting: A Benchmark Study on Soft Segmentation Method for Pulmonary Nodules Applied in Computed Tomography

Lin Wang, Xiufen Ye, Donghao Zhang +9

Usually, lesions are not isolated but are associated with the surrounding tissues. For example, the growth of a tumour can depend on or infiltrate into the surrounding tissues. Due…

eess.IV2022

3D Matting: A Soft Segmentation Method Applied in Computed Tomography

Lin Wang, Xiufen Ye, Donghao Zhang +7

Three-dimensional (3D) images, such as CT, MRI, and PET, are common in medical imaging applications and important in clinical diagnosis. Semantic ambiguity is a typical feature of…

cs.CV2022★ 3 cited

Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization

Wei Feng, Lin Wang, Lie Ju +4

Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks. However, these methods focus only on…

cs.CV2022

Flexible Sampling for Long-tailed Skin Lesion Classification

Lie Ju, Yicheng Wu, Lin Wang +5

Most of the medical tasks naturally exhibit a long-tailed distribution due to the complex patient-level conditions and the existence of rare diseases. Existing long-tailed learning…

cs.CV2021★ 2 cited

Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition

Lie Ju, Zhen Yu, Lin Wang +4

In the real world, medical datasets often exhibit a long-tailed data distribution (i.e., a few classes occupy the majority of the data, while most classes have only a limited numbe…

eess.IV2021★ 4 cited

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…