1 citations · 1 across the 5 of their papers we have counts for
5 papers
Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model
Ziyuan Yang, Yingyu Chen, Zhiwen Wang +3
Reducing radiation doses benefits patients, however, the resultant low-dose computed tomography (LDCT) images often suffer from clinically unacceptable noise and artifacts. While d…
Plaintext-Free Deep Learning for Privacy-Preserving Medical Image Analysis via Frequency Information Embedding
Mengyu Sun, Ziyuan Yang, Maosong Ran +3
In the fast-evolving field of medical image analysis, Deep Learning (DL)-based methods have achieved tremendous success. However, these methods require plaintext data for training…
Privacy-Preserving Encrypted Low-Dose CT Denoising
Ziyuan Yang, Huijie Huangfu, Maosong Ran +3
Deep learning (DL) has made significant advancements in tomographic imaging, particularly in low-dose computed tomography (LDCT) denoising. A recent trend involves servers training…
EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation
Yingyu Chen, Ziyuan Yang, Chenyu Shen +3
Recently, uncertainty-aware methods have attracted increasing attention in semi-supervised medical image segmentation. However, current methods usually suffer from the drawback tha…
One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline
Zhiwen Wang, Wenjun Xia, Zexin Lu +5
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow. It is easy to notic…