16 citations · 70 across the 14 of their papers we have counts for
14 papers
Base-Detail Feature Learning Framework for Visible-Infrared Person Re-Identification
Zhihao Gong, Lian Wu, Yong Xu
Visible-infrared person re-identification (VIReID) provides a solution for ReID tasks in 24-hour scenarios; however, significant challenges persist in achieving satisfactory perfor…
Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization
Chengli Tan, Yubo Zhou, Haishan Ye +7
Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving. However, many studies suggest that they are prone…
From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
Yuncheng Jiang, Chun-Mei Feng, Jinke Ren +15
Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on phy…
Cross-Modal Vertical Federated Learning for MRI Reconstruction
Yunlu Yan, Hong Wang, Yawen Huang +5
Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from d…
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…