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Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation by Zhou, Yinchi; Chen, Tianqi; Hou, Jun; Xie, Huidong; Dvornek, Nicha C.; Zhou, S. Kevin; Wilson, David L.; Duncan, James S.; Liu, Chi; Zhou, Bo is a scholarly article available to read on EtoBox.

What is Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation about?

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 Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating re

Author

Zhou, Yinchi; Chen, Tianqi; Hou, Jun; Xie, Huidong; Dvornek, Nicha C.; Zhou, S. Kevin; Wilson, David L.; Duncan, James S.; Liu, Chi; Zhou, Bo

Published

2024

Language

EN