3 citations · 8 across the 7 of their papers we have counts for
7 papers
Generalized Robust Fundus Photography-based Vision Loss Estimation for High Myopia
Zipei Yan, Zhile Liang, Zhengji Liu +5
High myopia significantly increases the risk of irreversible vision loss. Traditional perimetry-based visual field (VF) assessment provides systematic quantification of visual loss…
Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representations
Ting Wang, Zipei Yan, Jizhou Li +3
The fusion of a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) has emerged as an effective technique for achieving HSI super-resolu…
Boosting of Implicit Neural Representation-based Image Denoiser
Zipei Yan, Zhengji Liu, Jizhou Li
Implicit Neural Representation (INR) has emerged as an effective method for unsupervised image denoising. However, INR models are typically overparameterized; consequently, these m…
Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation
Shuai Wang, Zipei Yan, Daoan Zhang +4
Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. In contrast, the IID hypothesis…
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation
Shuai Wang, Daoan Zhang, Zipei Yan +2
Source-free domain adaptation aims to adapt deep neural networks using only pre-trained source models and target data. However, accessing the source model still has a potential con…
Prototype Knowledge Distillation for Medical Segmentation with Missing Modality
Shuai Wang, Zipei Yan, Daoan Zhang +3
Multi-modality medical imaging is crucial in clinical treatment as it can provide complementary information for medical image segmentation. However, collecting multi-modal data in…
Feature Alignment and Uniformity for Test Time Adaptation
Shuai Wang, Daoan Zhang, Zipei Yan +2
Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of distribution test domain samples. In this setting, the model can only access online unlabeled te…