14 citations · 67 across the 21 of their papers we have counts for
21 papers
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction
Yunlu Yan, Chun-Mei Feng, Yuexiang Li +2
While multi-modal learning has been widely used for MRI reconstruction, it relies on paired multi-modal data which is difficult to acquire in real clinical scenarios. Especially in…
Rethinking Client Drift in Federated Learning: A Logit Perspective
Yunlu Yan, Chun-Mei Feng, Mang Ye +5
Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. However, the real-world non-IID data will lead to c…
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
Chun-Mei Feng, Kai Yu, Yong Liu +2
Benefiting from prompt tuning, recent years have witnessed the promising performance of pre-trained vision-language models, e.g., CLIP, on versatile downstream tasks. In this paper…
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
Xinghong Liu, Yi Zhou, Tao Zhou +2
Universal domain adaptation (UniDA) aims to address domain and category shifts across data sources. Recently, due to more stringent data restrictions, researchers have introduced s…
Learning Federated Visual Prompt in Null Space for MRI Reconstruction
Chun-Mei Feng, Bangjun Li, Xinxing Xu +3
Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy…
Reliable Federated Disentangling Network for Non-IID Domain Feature
Meng Wang, Kai Yu, Chun-Mei Feng +6
Federated learning (FL), as an effective decentralized distributed learning approach, enables multiple institutions to jointly train a model without sharing their local data. Howev…
Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images
Meng Wang, Kai Yu, Chun-Mei Feng +6
Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, background noise interference, etc., in the task of retina…
Prompt-driven efficient Open-set Semi-supervised Learning
Haoran Li, Chun-Mei Feng, Tao Zhou +2
Open-set semi-supervised learning (OSSL) has attracted growing interest, which investigates a more practical scenario where out-of-distribution (OOD) samples are only contained in…
Specificity-Preserving Federated Learning for MR Image Reconstruction
Chun-Mei Feng, Yunlu Yan, Shanshan Wang +3
Federated learning (FL) can be used to improve data privacy and efficiency in magnetic resonance (MR) image reconstruction by enabling multiple institutions to collaborate without…
Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality
Chun-Mei Feng, Huazhu Fu, Tianfei Zhou +3
Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vuln…
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
Chun-Mei Feng, Yunlu Yan, Kai Yu +3
Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information,…
DONet: Dual-Octave Network for Fast MR Image Reconstruction
Chun-Mei Feng, Zhanyuan Yang, Huazhu Fu +3
Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multi…
Multi-Modal Transformer for Accelerated MR Imaging
Chun-Mei Feng, Yunlu Yan, Geng Chen +3
Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from it…
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
Chun-Mei Feng, Yunlu Yan, Huazhu Fu +2
The core problem of Magnetic Resonance Imaging (MRI) is the trade off between acceleration and image quality. Image reconstruction and super-resolution are two crucial techniques i…
Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network
Chun-Mei Feng, Huazhu Fu, Shuhao Yuan +1
Super-resolution (SR) plays a crucial role in improving the image quality of magnetic resonance imaging (MRI). MRI produces multi-contrast images and can provide a clear display of…
Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction
Chun-Mei Feng, Zhanyuan Yang, Geng Chen +2
Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration by obtaining multiple undersampled images simultaneously through parallel imaging h…
Coupled-Projection Residual Network for MRI Super-Resolution
Chun-Mei Feng, Kai Wang, Shijian Lu +3
Magnetic Resonance Imaging(MRI) has been widely used in clinical application and pathology research by helping doctors make more accurate diagnoses. On the other hand, accurate dia…
Robust Classification with Sparse Representation Fusion on Diverse Data Subsets
Chun-Mei Feng, Yong Xu, Zuoyong Li +1
Sparse Representation (SR) techniques encode the test samples into a sparse linear combination of all training samples and then classify the test samples into the class with the mi…
Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data
Chun-Mei Feng, Yong Xu, Jin-Xing Liu +2
Principal Component Analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been pro…
Iris R-CNN: Accurate Iris Segmentation in Non-cooperative Environment
Chunyang Feng, Yufeng Sun, Xin Li
Despite the significant advances in iris segmentation, accomplishing accurate iris segmentation in non-cooperative environment remains a grand challenge. In this paper, we present…
Dual Graph-Laplacian PCA: A Closed-Form Solution for Bi-clustering to Find "Checkerboard" Structures on Gene Expression Data
Jin-Xing Liu, Chun-Mei Feng, Xiang-Zhen Kong +1
In the context of cancer, internal "checkerboard" structures are normally found in the matrices of gene expression data, which correspond to genes that are significantly up- or dow…