GAM-UNet: incorporating global attention mechanism into U-Net for improved medical image segmentation

Sihao Xiang; Peng Liu

doi:10.1117/12.2681192

8 June 2023 GAM-UNet: incorporating global attention mechanism into U-Net for improved medical image segmentation

Sihao Xiang, Peng Liu

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127071S (2023) https://doi.org/10.1117/12.2681192
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Automatic segmentation of targets from medical images is a difficult task because of the high complexity of medical images and the lack of simple linear features. U-Net is widely used in the field of medical image segmentation for its state-of-the-art performance. However, there are still many problems, the most serious of which are the lack of accuracy of image segmentation and the large amount of information loss. To alleviate these deficiencies, we propose a UNetbased image segmentation model GAM-UNet, which has the same coder-decoder structure. The Global Attention Mechanism (GAM) is introduced and embedded into the encoding part of the proposed model to automatically adjust the weights of the feature maps. Evaluation results on the public datasets Data Science Bowl and CVC-ClinicDB show that our model performs better than other U-Net variants. After additional analysis, our proposed model is more hardware friendly and allows for faster medical diagnosis with a slight increase in accuracy. Our proposed approach can also be extended to other image processing tasks, such as object detection and tracking.

Citation Download Citation

Sihao Xiang and Peng Liu "GAM-UNet: incorporating global attention mechanism into U-Net for improved medical image segmentation", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127071S (8 June 2023); https://doi.org/10.1117/12.2681192

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