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• W3048798122 abstract "Recently, deep learning approaches have been extensively studied for low-dose CT denoising thanks to its superior performance despite the fast computational time. In particular, cycleGAN has been demonstrated as a powerful unsupervised learning scheme to improve the low-dose CT image quality without requiring matched high-dose reference data. Unfortunately, one of the main limitations of the cycleGAN approach is that it requires two deep neural network generators at the training phase, although only one of them is used at the inference phase. The secondary auxiliary generator is needed to enforce the cycle-consistency, but the additional memory requirement and increases of the learnable parameters are the main huddles for cycleGAN training. To address this issue, here we propose a novel cycleGAN architecture using a single switchable generator. In particular, a single generator is implemented using adaptive instance normalization (AdaIN) layers so that the baseline generator converting a low-dose CT image to a routine-dose CT image can be switched to a generator converting high-dose to low-dose by simply changing the AdaIN code. Thanks to the shared baseline network, the additional memory requirement and weight increases are minimized, and the training can be done more stably even with small training data. Experimental results show that the proposed method outperforms the previous cycleGAN approaches while using only about half the parameters." @default.
• W3048798122 created "2020-08-18" @default.
• W3048798122 creator A5012644755 @default.
• W3048798122 creator A5030881632 @default.
• W3048798122 date "2020-08-13" @default.
• W3048798122 modified "2023-09-25" @default.
• W3048798122 title "AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising" @default.
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• W3048798122 doi "https://doi.org/10.48550/arxiv.2008.05753" @default.
• W3048798122 hasPublicationYear "2020" @default.
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