Multi-Scale Group Agent Attention-Based Graph Convolutional Decoding Networks for 2D Medical Image Segmentation

Zhichao Wang, Lin Guo, Shuchang Zhao, Shiqing Zhang, Xiaoming Zhao, Jiangxiong Fang, Guoyu Wang, Hongsheng Lu, Jun Yu, Qi Tian

Abstract: Automated medical image segmentation plays a crucial role in assisting doctors in diagnosing diseases. Feature decoding is a critical yet challenging issue for medical image segmentation. To address this issue, this work proposes a novel feature decoding network, called multi-scale group agent attention-based graph convolutional decoding networks (MSGAA-GCDN), to learn local-global features in graph structures for 2D medical image segmentation. The proposed MSGAA-GCDN combines graph convolutional network (GCN) and a lightweight multi-scale group agent attention (MSGAA) mechanism to represent features globally and locally within a graph structure. Moreover, in skip connections a simple yet efficient attention-based upsampling convolution fusion (AUCF) module is designed to enhance encoder-decoder feature fusion in both channel and spatial dimensions. Extensive experiments are conducted on three typical medical image segmentation tasks, namely Synapse abdominal multi-organs, Cardiac organs, and Polyp lesions. Experimental results demonstrate that the proposed MSGAA-GCDN outperforms the state-of-the-art methods, and the designed MSGAA is a lightweight yet effective attention architecture. The proposed MSGAA-GCDN can be easily taken as a plug-and-play decoder cascaded with other encoders for general medical image segmentation tasks.

External IDs:doi:10.1109/jbhi.2024.3523112