PET Denoising and Uncertainty Estimation Based on NVAE Model

Cui, Juan; Xie, Yutong; Gong, Kuang; Kim, Kyungsang; Yang, Jaewon; Larson, Peder E. Z.; Hope, Thomas A.; Behr, Spencer C.; Seo, Youngho; Liu, Huafeng; Li, Quanzheng

doi:10.1109/nss/mic44867.2021.9875487

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“…Hung et al [2] adapted some of the features from NVAE into their hierarchical conditional VAE for ultrasound image inpainting. Cui et al [38] adopted NVAE in positron emission tomography (PET) scan image denoising and uncertainty estimation. As for the NF models, Grover et al [39] proposed AlignFlow based on a similar concept with NF models instead of GANs.…”

Section: Related Workmentioning

confidence: 99%

Med-cDiff: Conditional Medical Image Generation with Diffusion Models

Hung,

Zhao,

Zheng

et al. 2023

Bioengineering

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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.

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Section: Related Workmentioning

confidence: 99%