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Low-Level Dataset Distillation for Medical Image Enhancement
Fengzhi Xu, Ziyuan Yang, Mengyu Sun +2
Medical image enhancement is clinically valuable, but existing methods require large-scale datasets to learn complex pixel-level mappings. However, the substantial training and sto…
Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval
Yi Xie, Ziyuan Yang, Yongqiang Huang +4
Android malware detection continues to face persistent challenges stemming from long-term concept drift and class imbalance, as evolving malicious behaviors and shifting usage patt…
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…
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…
Dark Distillation: Backdooring Distilled Datasets without Accessing Raw Data
Ziyuan Yang, Ming Yan, Yi Zhang +1
Dataset distillation (DD) enhances training efficiency and reduces bandwidth by condensing large datasets into smaller synthetic ones. It enables models to achieve performance comp…
FedRIR: Rethinking Information Representation in Federated Learning
Yongqiang Huang, Zerui Shao, Ziyuan Yang +2
Mobile and Web-of-Things (WoT) devices at the network edge generate vast amounts of data for machine learning applications, yet privacy concerns hinder centralized model training.…