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GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image Segmentation

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Medical Image Understanding and Analysis (MIUA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14859))

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Abstract

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.

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  • 24 July 2024

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India

    Ayush Roy

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Payel Pramanik & Ram Sarkar

  3. Department of Electronics and Communication Engineering, International Institute of Information Technology, Chhattisgarh, India

    Sohom Ghosal

  4. Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, China

    Dmitrii Kaplun

  5. Department of Automation and Control Processes, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg, Russia

    Daria Valenkova & Dmitrii Kaplun

Authors
  1. Ayush Roy
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  2. Payel Pramanik
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  3. Sohom Ghosal
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  4. Daria Valenkova
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  5. Dmitrii Kaplun
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  6. Ram Sarkar
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Corresponding author

Correspondence to Dmitrii Kaplun .

Editor information

Editors and Affiliations

  1. Manchester Metropolitan University, Manchester, Lancashire, UK

    Moi Hoon Yap

  2. Manchester Metropolitan University, Manchester, UK

    Connah Kendrick

  3. Edge Hill University, Ormskirk, UK

    Ardhendu Behera

  4. Aberystwyth University, Aberystwyth, UK

    Timothy Cootes

  5. Aberystwyth University, Aberystwyth, UK

    Reyer Zwiggelaar

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Roy, A., Pramanik, P., Ghosal, S., Valenkova, D., Kaplun, D., Sarkar, R. (2024). GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image Segmentation. In: Yap, M.H., Kendrick, C., Behera, A., Cootes, T., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2024. Lecture Notes in Computer Science, vol 14859. Springer, Cham. https://doi.org/10.1007/978-3-031-66955-2_21

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  • DOI: https://doi.org/10.1007/978-3-031-66955-2_21

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