Target-Guided Diffusion Models for Unpaired Cross-modality Medical Image Translation

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Target-Guided Diffusion Models for Unpaired Cross-modality Medical Image Translation
Title:
Target-Guided Diffusion Models for Unpaired Cross-modality Medical Image Translation
Journal Title:
IEEE Journal of Biomedical and Health Informatics
Keywords:
Publication Date:
25 April 2024
Citation:
Luo, Y., Yang, Q., Liu, Z., Shi, Z., Huang, W., Zheng, G., & Cheng, J. (2024). Target-Guided Diffusion Models for Unpaired Cross-modality Medical Image Translation. IEEE Journal of Biomedical and Health Informatics, 1–10. https://doi.org/10.1109/jbhi.2024.3393870
Abstract:
In a clinical setting, the acquisition of certain medical image modality is often unavailable due to various considerations such as cost, radiation, etc. Therefore, unpaired cross-modality translation techniques, which involve training on the unpaired data and synthesizing the target modality with the guidance of the acquired source modality, are of great interest. Previous methods for synthesizing target medical images are to establish one-shot mapping through generative adversarial networks (GANs). As promising alternatives to GANs, diffusion models have recently received wide interests in generative tasks. In this paper, we propose a target-guided diffusion model (TGDM) for unpaired cross-modality medical image translation. For training, to encourage our diffusion model to learn more visual concepts, we adopted a perception prioritized weight scheme (P2W) to the training objectives. For sampling, a pre-trained classifier is adopted in the reverse process to relieve modality-specific remnants from source data. Experiments on both brain MRI-CT and prostate MRI-US datasets demonstrate that the proposed method achieves a visually realistic result that mimics a vivid anatomical section of the target organ. In addition, we have also conducted a subjective assessment based on the synthesized samples to further validate the clinical value of TGDM.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AI3
Grant Reference no. : C231118001

This research / project is supported by the A*STAR - AI3
Grant Reference no. : C211118006
Description:
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2168-2208
2168-2194
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