11 citations · 25 across the 11 of their papers we have counts for
11 papers
Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering
Wei Feng, Lie Ju, Lin Wang +2
Existing deep learning models have achieved promising performance in recognizing skin diseases from dermoscopic images. However, these models can only recognize samples from predef…
Universal Semi-Supervised Learning for Medical Image Classification
Lie Ju, Yicheng Wu, Wei Feng +4
Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning met…
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…
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…
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…
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…
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…
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…
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…
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…
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…