activity
20242026
most cited2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

PPDM: Pixel Puzzling Diffusion Model for Speed and Memory Efficient Volumetric Medical Image Translation

Tianqi Chen, Jun Hou, Yinchi Zhou +3

Diffusion models have demonstrated superior fidelity for medical image-to-image translation, but their extension to high-resolution 3D volumes is severely constrained by prohibitiv…

cs.CV2026

AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning

Zifei Dong, Wenjie Wu, Jinkui Hao +3

Robust anatomical segmentation of chest X-rays (CXRs) remains challenging due to the scarcity of comprehensive annotations and the substantial variability of real-world acquisition…

eess.IV2025

Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising

Yinchi Zhou, Huidong Xie, Menghua Xia +12

Low-count positron emission tomography (LCPET) imaging can reduce patients' exposure to radiation but often suffers from increased image noise and reduced lesion detectability, nec…

cs.CV20242 cited

2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction

Tianqi Chen, Jun Hou, Yinchi Zhou +8

Positron Emission Tomography (PET) is an important clinical imaging tool but inevitably introduces radiation hazards to patients and healthcare providers. Reducing the tracer injec…

eess.IV2024

Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation

Yinchi Zhou, Tianqi Chen, Jun Hou +7

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include…