Abstract
PURPOSE: The complexity of convolutional neural networks (CNNs) can lead to improved segmentation accuracy in medical image analysis but also results in increased network complexity and training challenges, especially under resource limitations. Conversely, lightweight models offer efficiency but often sacrifice accuracy. This paper addresses the challenge of balancing efficiency and accuracy by proposing LightAWNet, a lightweight adaptive weighting neural network for medical image segmentation. METHODS: We designed LightAWNet with an efficient inverted bottleneck encoder block optimized by spatial attention. A two-branch strategy is employed to separately extract detailed and spatial features for fusion, enhancing the reusability of model feature maps. Additionally, a lightweight optimized up-sampling operation replaces traditional transposed convolution, and channel attention is utilized in the decoder to produce more accurate outputs efficiently. RESULTS: Experimental results on the LiTS2017, MM-WHS, ISIC2018, and Kvasir-SEG datasets demonstrate that LightAWNet achieves state-of-the-art performance with only 2.83 million parameters. Our model significantly outperforms existing methods in terms of segmentation accuracy, highlighting its effectiveness in maintaining high performance with reduced complexity. CONCLUSIONS: LightAWNet successfully balances efficiency and accuracy in medical image segmentation. The innovative use of spatial attention, dual-branch feature extraction, and optimized up-sampling operations contribute to its superior performance. These findings offer valuable insights for the development of resource-efficient yet highly accurate segmentation models in medical imaging. The code will be made available at https://github.com/zjmiaprojects/lightawnet upon acceptance for publication.