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

53 citations · 123 across the 11 of their papers we have counts for

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8 papers · 1 filter

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…

cs.CV2021★ 2 cited

Medical Matting: A New Perspective on Medical Segmentation with Uncertainty

Lin Wang, Lie Ju, Xin Wang +8

It is difficult to accurately label ambiguous and complex shaped targets manually by binary masks. The weakness of binary mask under-expression is highlighted in medical image segm…

cs.CV2021★ 1 cited

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…

cs.CV2021★ 11 cited

Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation

Lie Ju, Xin Wang, Lin Wang +6

Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognit…

cs.CV2020★ 53 cited

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…

cs.CV2020★ 46 cited

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…