Hung Alex Ling Yu, Zhao Kai, Zheng Haoxin, Yan Ran, Raman Steven S, Terzopoulos Demetri, Sung Kyunghyun
Computer Science Department, University of California, Los Angeles, CA 90095, USA.
Department of Radiology, University of California, Los Angeles, CA 90095, USA.
Bioengineering (Basel). 2023 Oct 28;10(11):1258. doi: 10.3390/bioengineering10111258.
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.
条件图像生成在医学图像分析中起着至关重要的作用，因为它在超分辨率、去噪和图像修复等任务中非常有效。扩散模型已被证明在自然图像生成中达到了最先进的水平，但在特定条件下的医学图像生成中尚未得到充分研究。此外，当前的医学图像生成模型存在各自的问题，限制了它们在各种医学图像生成任务中的应用。在本文中，我们介绍了使用条件去噪扩散概率模型（cDDPMs）进行医学图像生成，该模型在多个医学图像生成任务中取得了最先进的性能。
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