Mixture-attention Siamese transformer for video polyp segmentation
Chen, Geng Yang, Junqing Pu, Xiaozhou Ji, Gepeng Xiong, Huan Pan, Yongsheng Cui, Hengfei Xia, Yong
Chen, Geng
Yang, Junqing
Pu, Xiaozhou
Ji, Gepeng
Xiong, Huan
Pan, Yongsheng
Cui, Hengfei
Xia, Yong
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Accurate segmentation of polyps from colonoscopy videos is of great significance to polyp treatment and early prevention of colorectal cancer. However, it is challenging due to the difficulties associated with modeling long-range spatio-temporal relationships within a colonoscopy video. In this paper, we address this challenging task with a novel Mixture-Attention Siamese Transformer (MAST), which explicitly models the long-range spatio-temporal relationships with a mixture-attention mechanism for accurate polyp segmentation. Specifically, we first construct a Siamese transformer architecture to jointly encode paired video frames for their feature representations. We then design a mixture-attention module to exploit the intra-frame and inter-frame correlations, enhancing the features with rich spatio-temporal relationships. Finally, the enhanced features are fed to two parallel decoders for predicting the segmentation maps. Extensive experiments on the large-scale SUN-SEG benchmark demonstrate the superior performance of MAST in comparison with the cutting-edge competitors. Our code is publicly available at https://github.com/Junqing-Yang/MAST.
Citation
G. Chen et al., “Mixture-attention Siamese transformer for video polyp segmentation,” Artif Intell Med, vol. 170, p. 103278, Dec. 2025, doi: 10.1016/J.ARTMED.2025.103278
Source
Artificial Intelligence in Medicine
Conference
Keywords
Attention mechanism, Colonoscopy, Transformer, Video polyp segmentation
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Source
Publisher
Elsevier