Abstract
Recently, pioneering work has improved segmentation performance by combining the self-attention (SA) mechanism with UNet. However, since SA can only model its own features in a single sample, it ignores the potential relevance of the whole dataset. Additionally, medical image datasets are typically small, making it crucial to obtain as many features as possible within a limited dataset. To address these problems, we propose the Multiple External Attention (MEA) module, which characterizes the overall dataset by mining correlations between different samples based on external concerns. Furthermore, our method applies the Squeeze-and-Excitation (SE) module for the first time to low-level feature extraction of medical images. By using MEA and SE, we construct MEA-TransUNet for accurate segmentation of medical images. We test our method on two datasets and the experimental results demonstrate its superior performance compared to other existing methods. Code and pre-trained models are coming soon.
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Acknowledgments
The paper is supported by the Natural Science Foundation of China (No. 62072388), Collaborative Project fund of Fuzhou-Xiamen-Quanzhou Innovation Zone (No. 3502ZCQXT202001), the industry guidance project foundation of science technology bureau of Fujian province in 2020 (No. 2020H0047), and Fujian Sunshine Charity Foundation.
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Cao, X., Yao, J., Hong, Q., Zhou, R. (2023). MEA-TransUNet: A Multiple External Attention Network for Multi-Organ Segmentation. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14262. Springer, Cham. https://doi.org/10.1007/978-3-031-44201-8_1
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