LMU-Net: lightweight U-shaped network for medical image segmentation

作     者：Ma, Ting Wang, Ke Hu, Feng 

作者机构：Southwest Petr Univ Chengdu Peoples R China Jiangsu Citron Biotech Co Ltd Nantong Peoples R China 

出 版 物：《MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING》 (医学和生物工程与计算)

年 卷 期：2024年第62卷第1期

页      面：61-70页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：Ahai Power Generation Branch of Yunnan Huadian Jinsha River Middle Water Power Development Co., Ltd China Huadian Corporation [CHDKJ22-02-88] 

主　　题：Medical image segmentation Deep learning LMU-Net Lightweight networks 

摘      要：Deep learning technology has been employed for precise medical image segmentation in recent years. However, due to the limited available datasets and real-time processing requirement, the inherently complicated structure of deep learning models restricts their application in the field of medical image processing. In this work, we present a novel lightweight LMU-Net network with improved accuracy for medical image segmentation. The multilayer perceptron (MLP) and depth-wise separable convolutions are adopted in both encoder and decoder of the LMU-Net to reduce feature loss and the number of training parameters. In addition, a lightweight channel attention mechanism and convolution operation with a larger kernel are introduced in the proposed architecture to further improve the segmentation performance. Furthermore, we employ batch normalization (BN) and group normalization (GN) interchangeably in our module to minimize the estimation shift in the network. Finally, the proposed network is evaluated and compared to other architectures on publicly accessible ISIC and BUSI datasets by carrying out robust experiments with sufficient ablation considerations. The experimental results show that the proposed LMU-Net can achieve a better overall performance than existing techniques by adopting fewer parameters.