activity
20222026
most citedPrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution

Liutao Yang, Zi Wang, Peiyuan Jing +5

PET super-resolution is highly under-constrained because paired multi-resolution scans from the same subject are rarely available, and effective resolution is determined by scanner…

cs.CV2026

ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selection

Chun-Wun Cheng, Yanqi Cheng, Peiyuan Jing +4

Medical image segmentation commonly relies on U-shaped encoder-decoder architectures such as U-Net, where skip connections preserve fine spatial detail by injecting high-resolution…

cs.CV2026

MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising

Peiyuan Jing, Chun-Wun Cheng, Liutao Yang +7

Low-dose Positron Emission Tomography (PET) reduces radiation exposure but suffers from severe noise and quantitative degradation. Diffusion-based denoising models achieve strong f…

cs.CV2025

DNA-Prior: Unsupervised Denoise Anything via Dual-Domain Prior

Yanqi Cheng, Chun-Wun Cheng, Jim Denholm +5

Medical imaging pipelines critically rely on robust denoising to stabilise downstream tasks such as segmentation and reconstruction. However, many existing denoisers depend on larg…

eess.IV2022★ 1 cited

PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis

Mohammadreza Amirian, Javier A. Montoya-Zegarra, Jonathan Gruss +5

With the spread of COVID-19 over the world, the need arose for fast and precise automatic triage mechanisms to decelerate the spread of the disease by reducing human efforts e.g. f…