6 papers
Trust the Unreliability: Inward Backward Dynamic Unreliability Driven Coreset Selection for Medical Image Classification
Yan Liang, Ziyuan Yang, Zhuxin Lei +3
Efficiently managing and utilizing large-scale medical imaging datasets with limited resources presents significant challenges. While coreset selection helps reduce computational c…
One CT Unified Model Training Framework to Rule All Scanning Protocols
Fengzhi Xu, Ziyuan Yang, Zexin Lu +4
Non-ideal measurement computed tomography (NICT), which lowers radiation at the cost of image quality, is expanding the clinical use of CT. Although unified models have shown promi…
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…
FedPalm: A General Federated Learning Framework for Closed- and Open-Set Palmprint Verification
Ziyuan Yang, Yingyu Chen, Chengrui Gao +3
Current deep learning (DL)-based palmprint verification models rely on centralized training with large datasets, which raises significant privacy concerns due to biometric data's s…
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…
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…