skip to main content
10.1145/3440943.3444357acmconferencesArticle/Chapter ViewAbstractPublication PagesiceaConference Proceedingsconference-collections
short-paper

Whole Slide Image Classification and Segmentation using Deep Learning

Published: 27 September 2021 Publication History

Abstract

Whole slide imaging is now being used across the world in pathology labs for an accurate diagnosis of biopsy specimens. However, due to the large size of these images, an automatic deep learning-based method is highly desirable for diagnosing. Herein, we propose a two-step methodology for the classification and segmentation of whole-slide image (WSI). First, the patches are extracted from the image and fed into deep learning based techniques like U-Net with its corresponding mask for the accurate segmentation. Further, the cancerous patches are trained for the classification task. During inference, the predicted segmented mask are evaluated in the classification model. Our experimental results demonstrated that the proposed methodology can be used for accurate segmentation and classification.

References

[1]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 234--241.
[2]
Mingxing Tan and Quoc V Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946 (2019).
[3]
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang. 2018. Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, 3--11.

Index Terms

  1. Whole Slide Image Classification and Segmentation using Deep Learning

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    ACM ICEA '20: Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications
    December 2020
    219 pages
    ISBN:9781450383042
    DOI:10.1145/3440943
    © 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 September 2021

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Classification
    2. Segmentation
    3. Whole-slide image

    Qualifiers

    • Short-paper
    • Research
    • Refereed limited

    Funding Sources

    • This work was supported by the GRRC program of Gyeonggi province.

    Conference

    ACM ICEA '20
    Sponsor:

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 45
      Total Downloads
    • Downloads (Last 12 months)7
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 15 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media