论文丨Zexin Lu, Wenjun Xia, Yongqiang Huang, Mingzheng Hou, Hu Chen, Jiliu Zhou, Hongming Shan*, and Yi Zhang*：M3NAS: Multi-scale and multi-level memory-efficient neural architecture search for low-dose CT denoising

本文（M3NAS: Multi-scale and multi-level memory-efficient neural architecture search for low-dose CT denoising）原载IEEE Transactions on Medical Imaging，由四川大学张意教授等科研人员创作，系四川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果，欢迎大家阅读。

Abstract:Lowering the radiation dose in computed tomography (CT) can greatly reduce the potential risk to public health. However, the reconstructed images from dose-reduced CT or low-dose CT (LDCT) suffer from severe noise which compromises the subsequent diagnosis and analysis. Recently, convolutional neural networks have achieved promising results in removing noise from LDCT images. The network architectures that are used are either handcrafted or built on top of conventional networks such as ResNet and U-Net. Recent advances in neural network architecture search (NAS) have shown that the network architecture has a dramatic effect on the model performance. This indicates that current network architectures for LDCT may be suboptimal. Therefore, in this paper, we make the first attempt to apply NAS to LDCT and propose a multi-scale and multi-level memory-efficient NAS for LDCT denoising, termed M3NAS. On the one hand, the proposed M3NAS fuses features extracted by different scale cells to capture multi-scale image structural details. On the other hand, the proposed M3NAS can search a hybrid cell- and network-level structure for better performance. In addition, M3NAS can effectively reduce the number of model parameters and increase the speed of inference. Extensive experimental results on two different datasets demonstrate that the proposed M3NAS can achieve better performance and fewer parameters than several state-of-the-art methods. In addition, we also validate the effectiveness of the multi-scale and multi-level architecture for LDCT denoising, and present further analysis for different configurations of super-net.

Zexin Lu, Wenjun Xia, Yongqiang Huang, Mingzheng Hou, Hu Chen, Jiliu Zhou, Hongming Shan*, and Yi Zhang*. M3NAS: Multi-scale and multi-level memory-efficient neural architecture search for low-dose CT denoising. IEEE Transactions on Medical Imaging, pp. 850-863, vol. 42, no. 3, 2023.（论文下载）