AdaIN-Based Tunable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising

Abstract

Recently, deep learning approaches using CycleGAN have been demonstrated as a powerful unsupervised learning scheme for low-dose CT denoising. 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 requirements and the increase in the number of learnable parameters are major hurdles for successful CycleGAN training. Despite the use of additional generator, CycleGAN only translates between two domains, so it is not possible to investigate the intermediate level of denoising. To address this issue, here we propose a novel tunable CycleGAN architecture using a single 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. Furthermore, by interpolating the AdaIN codes between the two domains, we can investigate various intermediate level of denoising results. Experimental results show that the proposed method outperforms the previous CycleGAN approaches while using only about half the parameters, and provide tunable denoising features that may be potentially useful in clinical environment.