GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image Segmentation Article Swipe
Ayush Roy
,
Payel Pramanik
,
Sohom Ghosal
,
Daria Valenkova
,
Dmitrii Kaplun
,
Ram Sarkar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.08604
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.08604
Breast cancer is a major global health concern. Pathologists face challenges in analyzing complex features from pathological images, which is a time-consuming and labor-intensive task. Therefore, efficient computer-based diagnostic tools are needed for early detection and treatment planning. This paper presents a modified version of MultiResU-Net for histopathology image segmentation, which is selected as the backbone for its ability to analyze and segment complex features at multiple scales and ensure effective feature flow via skip connections. The modified version also utilizes the Gaussian distribution-based Attention Module (GdAM) to incorporate histopathology-relevant text information in a Gaussian distribution. The sampled features from the Gaussian text feature-guided distribution highlight specific spatial regions based on prior knowledge. Finally, using the Controlled Dense Residual Block (CDRB) on skip connections of MultiResU-Net, the information is transferred from the encoder layers to the decoder layers in a controlled manner using a scaling parameter derived from the extracted spatial features. We validate our approach on two diverse breast cancer histopathology image datasets: TNBC and MonuSeg, demonstrating superior segmentation performance compared to state-of-the-art methods. The code for our proposed model is available on https://github.com/AyushRoy2001/GRU-Net.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.08604
- https://arxiv.org/pdf/2406.08604
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399695092
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4399695092Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.08604Digital Object Identifier
- Title
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GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image SegmentationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-06-12Full publication date if available
- Authors
-
Ayush Roy, Payel Pramanik, Sohom Ghosal, Daria Valenkova, Dmitrii Kaplun, Ram SarkarList of authors in order
- Landing page
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https://arxiv.org/abs/2406.08604Publisher landing page
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https://arxiv.org/pdf/2406.08604Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2406.08604Direct OA link when available
- Concepts
-
Histopathology, Net (polyhedron), Artificial intelligence, Segmentation, Computer vision, Computer science, Medicine, Mathematics, Pathology, 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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