# Med-cDiff: Conditional Medical Image Generation with Diffusion Models

Article · Alex Ling Yu Hung, Kai Zhao, Haoxin Zheng, Ran Yan, Steven S. Raman, Demetri Terzopoulos, Kyunghyun Sung

- Archive id: doi:10.3390/bioengineering10111258
- Published: 2023-10-28
- Venue: Bioengineering
- DOI: https://doi.org/10.3390/bioengineering10111258
- PMID: 38002382
- Citations: 47
- Source URL: https://doi.org/10.3390/bioengineering10111258
- PDF: https://www.mdpi.com/2306-5354/10/11/1258/pdf?version=1698740119
- Provenance: openalex

## Abstract

Conditional image generation plays a vital role in medical image analysis as it is effective in tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models have been shown to perform at a state-of-the-art level in natural image generation, but they have not been thoroughly studied in medical image generation with specific conditions. Moreover, current medical image generation models have their own problems, limiting their usage in various medical image generation tasks. In this paper, we introduce the use of conditional Denoising Diffusion Probabilistic Models (cDDPMs) for medical image generation, which achieve state-of-the-art performance on several medical image generation tasks.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [Generative & Multimodal Models](https://space-kitty.com/topics/generative-multimodal)

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