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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…
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images
Kang Zhou, Jing Li, Weixin Luo +6
Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a sel…
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,…
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers
Bo Dong, Wenhai Wang, Deng-Ping Fan +3
Most polyp segmentation methods use CNNs as their backbone, leading to two key issues when exchanging information between the encoder and decoder: 1) taking into account the differ…
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…