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Denoising Unpaired Low Dose CT Images with Self-Ensembled CycleGAN
This document presents a poster on denoising unpaired low dose CT images using a self-ensembled CycleGAN, focusing on the comparison between low and high dose images, as well as the architecture and training details. The authors address issues with naive implementations while showcasing results through input-output comparisons. Additionally, the presentation concludes with future directions and invites questions from the audience.
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Similar to Denoising Unpaired Low Dose CT Images with Self-Ensembled CycleGAN
Denoising Unpaired Low Dose CT Images with Self-Ensembled CycleGAN
- 1. Denoising Unpaired Low DoseCT Images with Self-Ensembled CycleGAN Joonhyung Lee, Sangjoon Park, Sun Kyoung You, Jong Chul Ye ISBI 2020 Workshop 1 Poster (SaPcPo): “Deep Learning for Biomedical Image Reconstruction”
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- 3. Low Dose vs.High Dose 3
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- 6. Issues with NaïveImplementation Input Output 6
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- 10. Training Details • TrainingEpochs at 200 × 256 𝑝𝑎𝑡𝑐ℎ 𝑠𝑖𝑧𝑒 × 256 𝑝𝑎𝑡𝑐ℎ 𝑠𝑖𝑧𝑒 . • One Adam Optimizer per domain (2 in total) with 𝛽1 = 0.5, 𝛽2 = 0.999. 10
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- 13. Difference Image 1 13 Output- Input
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- 15. Difference Image 2 15 Output- Input
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- 17. Issues & FutureDirection 17 Input Output
- 18. Thank You forListening! • The slides for this presentation are available at https://www.slideshare.net/ssuserc416e2/presentations. • Please contact the authors if you have any questions. 18
Editor's Notes
- #5 However, unlike in most denoising projects, we cannot use supervised learning with paired noisy and clean images as this would expose patients to unnecessary radiation. However, unpaired images of clean and noisy images at different radiation doses are easily available. Therefore, we are faced with the problem of how to learn denoising using unpaired image data.
- #9 First, to solve the problem with image segments disappearing and bright spots, we simply reduced the input image patch size. Our data consist of images that are at least 512 pixels in length. However, most GAN models have difficulty in learning very large images. Because denoising is a low-level task, using small patches can produce adequate quality results. Second, to stabilize training, we included spectral normalization in both generator and discriminator. We find that using both is better than using either alone. For the generator architecture, we use the RRDB network modified to have identically sized images. We use 32 channels for all convolutional layers. The model has no feature normalization layers to reduce computation and preserve the original range of values. Pooling layers are also removed. We found that removing pooling layers both improved outcomes and reduced convergence times.