Algorithm for polyp segmentation with local encoding and decoding fusion and multi-scale attention

Qi Wu; Changming Zhu

doi:10.1117/12.2682582

26 May 2023 Algorithm for polyp segmentation with local encoding and decoding fusion and multi-scale attention

Qi Wu, Changming Zhu

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127002M (2023) https://doi.org/10.1117/12.2682582
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

In recent years, the application of medical image semantic segmentation tasks in medical diagnosis and treatment planning has received widespread attention from the research community. The High-Resolution Network (HRNet) has good adaptability to high-resolution and high-scale medical images. In this paper, a novel high-resolution serial feature fusion encoding and decoding structure is proposed, and a CBAM attention mechanism is fused to construct a module that can jointly focus on spatial, channel, and multi-scale hierarchical information, which can improve the feature representation ability of the model and effectively reduce parameter complexity. We use the HRNet architecture to construct our model. Experimental results show that our method achieves MIoU coefficient of 98.44% on the Kvasir-SEG dataset, which is 1.43 percentage points higher than the original HR-Net model, validating the effectiveness and reliability of our method.

Citation Download Citation

Qi Wu and Changming Zhu "Algorithm for polyp segmentation with local encoding and decoding fusion and multi-scale attention", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127002M (26 May 2023); https://doi.org/10.1117/12.2682582

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