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research-article

Intelligent Gastric Histopathology Image Classification Using Hierarchical Conditional Random Field based Attention Mechanism

Authors:

Yong ZhangAuthors Info & Claims

ICMLC '21: Proceedings of the 2021 13th International Conference on Machine Learning and Computing

Pages 330 - 335

https://doi.org/10.1145/3457682.3457733

Published: 21 June 2021 Publication History

Abstract

In this paper, an Intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed, which can be applied to the Gastric Histopathology Image Classification (GHIC) tasks to assist pathologists in medical diagnosis. However, there exists redundant information in a weakly supervised learning mission. Thus, designing the network that can extract effective distinguishing features has become the focus of research. The HCRF-AM model consists of attention mechanism (AM) module and image classification (IC) module. First, in the AM module, an HCRF model is built to extract attention areas. Then, a convolutional neural network (CNN) model is trained with the attention region selected. Thirdly, an algorithm called classification probability based Ensemble Learning (EL) is used to obtain the image-level result from patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathological dataset with 700 images.

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Cited By

Huang JSaw SHe TYang RQin YChen YKiong L(2024)DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology ImagesIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.333470928:8(4534-4543)Online publication date: Aug-2024
https://doi.org/10.1109/JBHI.2023.3334709
Jing YLi CDu TJiang TSun HYang JShi LGao MGrzegorzek MLi X(2023)A comprehensive survey of intestine histopathological image analysis using machine vision approachesComputers in Biology and Medicine10.1016/j.compbiomed.2023.107388165:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107388
Basu ASenapati PDeb MRai RDhal K(2023)A survey on recent trends in deep learning for nucleus segmentation from histopathology imagesEvolving Systems10.1007/s12530-023-09491-315:1(203-248)Online publication date: 6-Mar-2023
https://doi.org/10.1007/s12530-023-09491-3
Show More Cited By

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A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification
Abstract
In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on distinguishing ...

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cover image ACM Other conferences

ICMLC '21: Proceedings of the 2021 13th International Conference on Machine Learning and Computing

February 2021

601 pages

ISBN:9781450389310

DOI:10.1145/3457682

Copyright © 2021 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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Publication History

Published: 21 June 2021

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National Natural Science Foundation of China

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ICMLC 2021

ICMLC 2021: 2021 13th International Conference on Machine Learning and Computing

February 26 - March 1, 2021

Shenzhen, China

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Cited By

Huang JSaw SHe TYang RQin YChen YKiong L(2024)DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology ImagesIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.333470928:8(4534-4543)Online publication date: Aug-2024
https://doi.org/10.1109/JBHI.2023.3334709
Jing YLi CDu TJiang TSun HYang JShi LGao MGrzegorzek MLi X(2023)A comprehensive survey of intestine histopathological image analysis using machine vision approachesComputers in Biology and Medicine10.1016/j.compbiomed.2023.107388165:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.compbiomed.2023.107388
Basu ASenapati PDeb MRai RDhal K(2023)A survey on recent trends in deep learning for nucleus segmentation from histopathology imagesEvolving Systems10.1007/s12530-023-09491-315:1(203-248)Online publication date: 6-Mar-2023
https://doi.org/10.1007/s12530-023-09491-3
Du TZhao H(2022)Texture Analysis of Enhanced MRI and Pathological Slides Predicts EGFR Mutation Status in Breast CancerBioMed Research International10.1155/2022/13766592022(1-15)Online publication date: 26-May-2022
https://doi.org/10.1155/2022/1376659
Li YWu XLi CLi XChen HSun CRahaman MYao YZhang YJiang T(2022)A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classificationApplied Intelligence10.1007/s10489-021-02886-252:9(9717-9738)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-021-02886-2

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