Full-scale Deeply Supervised Attention Network for Segmenting COVID-19 Lesions Article Swipe
Pallabi Dutta
,
Sushmita Mitra
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.15571
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.15571
Automated delineation of COVID-19 lesions from lung CT scans aids the diagnosis and prognosis for patients. The asymmetric shapes and positioning of the infected regions make the task extremely difficult. Capturing information at multiple scales will assist in deciphering features, at global and local levels, to encompass lesions of variable size and texture. We introduce the Full-scale Deeply Supervised Attention Network (FuDSA-Net), for efficient segmentation of corona-infected lung areas in CT images. The model considers activation responses from all levels of the encoding path, encompassing multi-scalar features acquired at different levels of the network. This helps segment target regions (lesions) of varying shape, size and contrast. Incorporation of the entire gamut of multi-scalar characteristics into the novel attention mechanism helps prioritize the selection of activation responses and locations containing useful information. Determining robust and discriminatory features along the decoder path is facilitated with deep supervision. Connections in the decoder arm are remodeled to handle the issue of vanishing gradient. As observed from the experimental results, FuDSA-Net surpasses other state-of-the-art architectures; especially, when it comes to characterizing complicated geometries of the lesions.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.15571
- https://arxiv.org/pdf/2210.15571
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4307537414Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2210.15571Digital Object Identifier
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Full-scale Deeply Supervised Attention Network for Segmenting COVID-19 LesionsWork title
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preprintOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-10-27Full publication date if available
- Authors
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Pallabi Dutta, Sushmita MitraList of authors in order
- Landing page
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https://arxiv.org/abs/2210.15571Publisher landing page
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https://arxiv.org/pdf/2210.15571Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2210.15571Direct OA link when available
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Computer science, Segmentation, Artificial intelligence, Scale (ratio), Pattern recognition (psychology), Path (computing), Scalar (mathematics), Mathematics, Cartography, Programming language, Geography, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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