Dual-Domain Feature Interaction Network for Automatic Colorectal Polyp Segmentation Article Swipe
Guanghui Yue
,
Yuanyan Li
,
Shangjie Wu
,
Bin Jiang
,
Tianwei Zhou
,
Weiqing Yan
,
Hanhe Lin
,
Tianfu Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tim.2024.3470962
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tim.2024.3470962
Recently, many deep neural network-based methods have been proposed for polyp segmentation. Nevertheless, most methods primarily analyze spatial information and usually fail to accurately localize polyps with inconsistent sizes, irregular shapes, and blurry boundaries. In this article, we propose a dual-domain feature interaction network (DFINet) for automatic polyp segmentation to overcome these difficulties. DFINet has an encoder-decoder structure that includes two key modules: a spatial and frequency feature interaction (SFFI) module and a boundary enhancement (BE) module. To learn shape-aware information, the SFFI module is deployed at each layer of the encoder, where spatial and frequency features are simultaneously extracted and fused using the attention mechanism. Such a module helps the network adjust to the polyps with irregular shapes and blurry boundaries. The BE module is used to enhance the boundary areas by integrating cross-layer features of SFFI modules with the prediction map of the adjacent high layer. Since there is no higher layer for the top layer, we integrate the multiscale features of the encoder to generate a prediction map for the BE module at the top layer. Such configuration helps the network handle the challenge of inconsistent sizes. By connecting the BE modules from top to bottom and applying deep supervision, DFINet can generate coarse-to-fine prediction maps. Results of both in-domain and out-of-domain tests show that DFINet achieves good segmentation results, with stronger learning ability and better generalization ability than 11 state-of-the-art methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tim.2024.3470962
- OA Status
- green
- Cited By
- 12
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402979381
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402979381Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tim.2024.3470962Digital Object Identifier
- Title
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Dual-Domain Feature Interaction Network for Automatic Colorectal Polyp SegmentationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Guanghui Yue, Yuanyan Li, Shangjie Wu, Bin Jiang, Tianwei Zhou, Weiqing Yan, Hanhe Lin, Tianfu WangList of authors in order
- Landing page
-
https://doi.org/10.1109/tim.2024.3470962Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://discovery.dundee.ac.uk/en/publications/6ce927d8-a799-46c7-baba-289b01539e43Direct OA link when available
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Feature (linguistics), Computer science, Artificial intelligence, Domain (mathematical analysis), Pattern recognition (psychology), Segmentation, Feature extraction, Computer vision, Image segmentation, Dual (grammatical number), Mathematics, Mathematical analysis, Literature, Linguistics, Philosophy, ArtTop concepts (fields/topics) attached by OpenAlex
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12Total citation count in OpenAlex
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2025: 11, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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