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Improving Histopathological Image Segmentation and Classification using Graph Convolution Network

Authors:

Chenyan ZhuAuthors Info & Claims

ICCPR '19: Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition

Pages 192 - 198

https://doi.org/10.1145/3373509.3373579

Published: 25 March 2020 Publication History

Abstract

In this paper, we present a system for segmentation and classification of breast cancer ROI images by integrating the idea of hierarchical processing of segmentation and classification tasks. The system is composed of a segmentation module and a GCN module, where the GCN module is designed to improve the performance of the classification result. The segmentation module is used to obtain the segmentation masks of the image patches of the ROI image, which are spliced to generate the segmenion result of the ROI image. The GCN module is used to capture the spatial and semantic dependencies among image patches of the ROI image, by constructing the graph using the segmentation masks of the image patches. Based on the learned features by the GCN module, the classification result of the ROI image can be obtained. Experimental results on the grand challenge on BreAst Cancer Histology images (BACH) 2018 dataset [17] show that, the proposed segmentation and classification method outperforms the winner of BACH 2018 significantly, which demonstrates the effectiveness of the proposed method.

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Cited By

Alnaggar OJagadale BSaif MGhaleb OAhmed AAqlan HAl-Ariki H(2024)Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysisArtificial Intelligence Review10.1007/s10462-024-10814-257:8Online publication date: 29-Jul-2024
https://doi.org/10.1007/s10462-024-10814-2
Alzoubi IBao GZheng YWang XGraeber M(2022)Artificial intelligence techniques for neuropathological diagnostics and researchNeuropathology10.1111/neup.1288043:4(277-296)Online publication date: 28-Nov-2022
https://doi.org/10.1111/neup.12880
Gao ZLu ZWang JYing SShi J(2022)A Convolutional Neural Network and Graph Convolutional Network Based Framework for Classification of Breast Histopathological ImagesIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2022.315367126:7(3163-3173)Online publication date: Jul-2022
https://doi.org/10.1109/JBHI.2022.3153671
Show More Cited By

Index Terms

Improving Histopathological Image Segmentation and Classification using Graph Convolution Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Object recognition
2. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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ICCPR '19: Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition

October 2019

522 pages

ISBN:9781450376570

DOI:10.1145/3373509

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Hebei University of Technology
Beijing University of Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 March 2020

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ICCPR '19

ICCPR '19: 2019 8th International Conference on Computing and Pattern Recognition

October 23 - 25, 2019

Beijing, China

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Cited By

Alnaggar OJagadale BSaif MGhaleb OAhmed AAqlan HAl-Ariki H(2024)Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysisArtificial Intelligence Review10.1007/s10462-024-10814-257:8Online publication date: 29-Jul-2024
https://doi.org/10.1007/s10462-024-10814-2
Alzoubi IBao GZheng YWang XGraeber M(2022)Artificial intelligence techniques for neuropathological diagnostics and researchNeuropathology10.1111/neup.1288043:4(277-296)Online publication date: 28-Nov-2022
https://doi.org/10.1111/neup.12880
Gao ZLu ZWang JYing SShi J(2022)A Convolutional Neural Network and Graph Convolutional Network Based Framework for Classification of Breast Histopathological ImagesIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2022.315367126:7(3163-3173)Online publication date: Jul-2022
https://doi.org/10.1109/JBHI.2022.3153671
Guan YZhang JTian KYang SDong PXiang JYang WHuang JZhang YHan X(2022)Node-aligned Graph Convolutional Network for Whole-slide Image Representation and Classification2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.01825(18791-18801)Online publication date: Jun-2022
https://doi.org/10.1109/CVPR52688.2022.01825
Li ZHuang KLiu LZhang Z(2022)Early detection of COPD based on graph convolutional network and small and weakly labeled dataMedical & Biological Engineering & Computing10.1007/s11517-022-02589-x60:8(2321-2333)Online publication date: 24-Jun-2022
https://doi.org/10.1007/s11517-022-02589-x

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