5 papers
Federated Learning for Large Models in Medical Imaging: A Comprehensive Review
Mengyu Sun, Ziyuan Yang, Yongqiang Huang +5
Artificial intelligence (AI) has demonstrated considerable potential in the realm of medical imaging. However, the development of high-performance AI models typically necessitates…
Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentation
Yingyu Chen, Ziyuan Yang, Ming Yan +4
Multi-modality (MM) semi-supervised learning (SSL) based medical image segmentation has recently gained increasing attention for its ability to utilize MM data and reduce reliance…
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…
Energizing Federated Learning via Filter-Aware Attention
Ziyuan Yang, Zerui Shao, Huijie Huangfu +5
Federated learning (FL) is a promising distributed paradigm, eliminating the need for data sharing but facing challenges from data heterogeneity. Personalized parameter generation…
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…