Abstract
Polyp segmentation is an essential step in the diagnosis and treatment of colorectal cancer. Due to the low contrast between the foreground and the background, the large difference in shape and size and the blurred boundary of the polyp, accurate polyp segmentation faces huge challenges. In this paper, we propose a Multi-scale Fusion Attention Network (MSFANet) for polyp segmentation. Firstly, we design the Gated Local Attention Module (GLAM) to connect the encoder and decoder to extract local features of polyps of different shapes and sizes. Secondly, the Positional Global Attention Module (PGAM) is constructed at the end of the encoder to capture the global dependency of context information. Finally, in the decoding stage, we present the Dilated Dense Connection (DDC) to enhance the transfer and reuse of features. Meanwhile, the Multi-scale Feature Fusion Attention Module (MFFAM) is designed to selectively aggregate multi-scale features and emphasize target features. In addition, we evaluate our MSFANet model on the public datasets Kvasir-SEG and EndoScene. Compared with other state-of-the-art methods, our model shows better segmentation results.
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Huang, D., Han, K., Xi, Y., Che, W. (2021). Multi-scale Fusion Attention Network for Polyp Segmentation. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_19
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