5 papers
T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images
Christopher Wiedeman, Anastasiia Sarmakeeva, Elena Sizikova +4
One of the key impediments for developing and assessing robust medical imaging algorithms is limited access to large-scale datasets with suitable annotations. Synthetic data genera…
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model
Chuang Niu, Christopher Wiedeman, Mengzhou Li +2
This study aims to improve photon counting CT (PCCT) image resolution using denoising diffusion probabilistic models (DDPM). Although DDPMs have shown superior performance when app…
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
Christopher Wiedeman, Chuang Niu, Mengzhou Li +3
Ultra-high resolution images are desirable in photon counting CT (PCCT), but resolution is physically limited by interactions such as charge sharing. Deep learning is a possible me…
Decorrelative Network Architecture for Robust Electrocardiogram Classification
Christopher Wiedeman, Ge Wang
Artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed. As it is…
Enabling Competitive Performance of Medical Imaging with Diffusion Model-generated Images without Privacy Leakage
Yongyi Shi, Wenjun Xia, Chuang Niu +2
Deep learning methods have impacted almost every research field, demonstrating notable successes in medical imaging tasks such as denoising and super-resolution. However, the prere…