Abstract
Polyp segmentation has been a challenging problem for researchers because it does not define a specific shape, color, or size. Traditional deep learning models, based on convolutional neural networks (CNNs), struggle to generalize well on unseen datasets. However, the Transformer architecture has shown promising potential in addressing medical problems by effectively capturing long-range dependencies through self-attention. This paper introduces the DAMFormer model based on Transformer for high accuracy while keeping lightness. The DAMFormer utilizes a Transformer encoder to extract better global information. The Transformer outputs are strategically fed into the ConvBlock and Enhanced Dual Attention Module to effectively capture high-frequency and low-frequency information. These outputs are further processed through the Effective Feature Fusion module to combine global and local features efficiently. In our experiment, five standard benchmark datasets were used Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-Larib.
H.T. Quang and M. Nguyen—These authors contributed equally to this work.
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Quang, H.T. et al. (2024). DAMFormer: Enhancing Polyp Segmentation Through Dual Attention Mechanism. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14450. Springer, Singapore. https://doi.org/10.1007/978-981-99-8070-3_8
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