Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation

TL;DR

This paper introduces a novel cascaded diffusion model that combines GANs and DMs for improved medical image translation, achieving high quality results with uncertainty estimation across multiple datasets.

Contribution

The proposed CMDM method innovatively integrates GAN priors with diffusion models and employs a multi-path cascade to enhance translation quality and uncertainty estimation in medical imaging.

Findings

High-quality translations comparable to state-of-the-art methods

Effective uncertainty estimation correlated with translation error

Demonstrated generalizability across multiple datasets

Abstract

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 residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.