MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation

TL;DR

MALUNet is a lightweight, multi-attention U-Net variant designed for skin lesion segmentation, achieving high accuracy with significantly reduced parameters and computational complexity suitable for clinical environments.

Contribution

The paper introduces four novel modules integrated into a U-Net architecture to create MALUNet, a lightweight model that balances performance and efficiency for skin lesion segmentation.

Findings

MALUNet improves segmentation metrics (mIoU and DSC) over UNet.

It reduces parameters and computational complexity by over 40 and 160 times.

Achieves state-of-the-art performance on ISIC datasets.

Abstract

Recently, some pioneering works have preferred applying more complex modules to improve segmentation performances. However, it is not friendly for actual clinical environments due to limited computing resources. To address this challenge, we propose a light-weight model to achieve competitive performances for skin lesion segmentation at the lowest cost of parameters and computational complexity so far. Briefly, we propose four modules: (1) DGA consists of dilated convolution and gated attention mechanisms to extract global and local feature information; (2) IEA, which is based on external attention to characterize the overall datasets and enhance the connection between samples; (3) CAB is composed of 1D convolution and fully connected layers to perform a global and local fusion of multi-stage features to generate attention maps at channel axis; (4) SAB, which operates on multi-stage features by a shared 2D convolution to generate attention maps at spatial axis. We combine four modules with our U-shape architecture and obtain a light-weight medical image segmentation model dubbed as MALUNet. Compared with UNet, our model improves the mIoU and DSC metrics by 2.39% and 1.49%, respectively, with a 44x and 166x reduction in the number of parameters and computational complexity. In addition, we conduct comparison experiments on two skin lesion segmentation datasets (ISIC2017 and ISIC2018). Experimental results show that our model achieves state-of-the-art in balancing the number of parameters, computational complexity and segmentation performances. Code is available at https://github.com/JCruan519/MALUNet.