Adaptive Non-Local Generative Adversarial Networks for Low-Dose CT Image Denoising
Linlin Yang, Hongying Liu, Fanhua Shang, Yuanyuan Liu
Abstract
Low-dose computed tomography (CT) has been widely used in medical diagnosis and treatment. Many deep networks have been proposed for low-dose CT denoising. The local receptive field of the convolution affects the network performance. For different input images, conventional neural networks always adopt a fixed number of channels which limits the performance of deep networks. To address these problems, we propose a channel-adaptive convolution and patch selection (CAPS) module to enhance the feature extraction of our network. CAPS enables our network to adaptively adjust the number of channels according to different inputs. Moreover, the concatenation of patches can expand the receptive field globally, so the shallow layer of our network can extract more global information. To further ensure the clarity of denoised images, we present a new wavelet loss function to the generator of our generative adversarial network. Compared with state-of-the-art methods, our network can obtain superior denoising results.
BibTeX
@inproceedings{icassp2023_adaptivenonlocal,
title = {Adaptive Non-Local Generative Adversarial Networks for Low-Dose CT Image Denoising},
author = {Linlin Yang and Hongying Liu and Fanhua Shang and Yuanyuan Liu},
booktitle = {ICASSP 2023},
year = {2023}
}