1 citations · 1 across the 5 of their papers we have counts for
5 papers
Xray2Xray: World Model from Chest X-rays with Volumetric Context
Zefan Yang, Xinrui Song, Xuanang Xu +4
Chest X-rays (CXRs) are the most widely used medical imaging modality and play a pivotal role in diagnosing diseases. However, as 2D projection images, CXRs are limited by structur…
Zero-Shot Low-dose CT Denoising via Sinogram Flicking
Yongyi Shi, Ge Wang
Many low-dose CT imaging methods rely on supervised learning, which requires a large number of paired noisy and clean images. However, obtaining paired images in clinical practice…
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing
Yongyi Shi, Ge Wang
Leveraging multi-center data for medical analytics presents challenges due to privacy concerns and data heterogeneity. While distributed approaches such as federated learning has g…
Manifold Topological Deep Learning for Biomedical Data
Xiang Liu, Zhe Su, Yongyi Shi +3
Recently, topological deep learning (TDL), which integrates algebraic topology with deep neural networks, has achieved tremendous success in processing point-cloud data, emerging a…
CT-based Anomaly Detection of Liver Tumors Using Generative Diffusion Prior
Yongyi Shi, Chuang Niu, Amber L. Simpson +3
CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify…