Staff View: A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0

A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0

The emergence of Healthcare 4.0 brings convenience to the diagnosis of gastric polyps patients. The computer aided gastric polyp detection model can automatically locate the position of gastric polyps in gastroscopic images, which helps endoscopists to detect gastric polyps in time and reduce the ra...

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Main Authors:	Huang, Junjun, Saw, Shier Nee, He, Tianran, Feng, Wei, Loo, Chu Kiong
Format:	Article
Published:	ELSEVIER 2024
Subjects:	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/44155/ https://doi.org/10.1016/j.jii.2023.100467
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spelling	my.um.eprints.441552024-06-11T04:57:42Z http://eprints.um.edu.my/44155/ A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0 Huang, Junjun Saw, Shier Nee He, Tianran Feng, Wei Loo, Chu Kiong QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering The emergence of Healthcare 4.0 brings convenience to the diagnosis of gastric polyps patients. The computer aided gastric polyp detection model can automatically locate the position of gastric polyps in gastroscopic images, which helps endoscopists to detect gastric polyps in time and reduce the rate of missed diagnosis. The deep learning model has achieved remarkable success in the field of gastroscopic images, however, it still has the following problems to be solved. Firstly, the model based on the convolutional neural network only analyzes the underlying pixels of the gastroscopic image to locate the polyp, which does not take into account the spatial and positional information contained in the anatomical structure of the gastroscopic image. Secondly, although the number of gastroscopic images is huge, the number of manually annotated gastric polyp images is very small, which makes the deep learning model prone to overfitting. Therefore, in this work, we propose a masked graph neural network model (MGNN) for real-time detecting the location of polyps in gastroscopic images in the Healthcare 4.0. The MGNN model novelly utilizes the graph structure and graph convolution operations to extract spatial location information and semantic information of the gastroscopic images. The information from masked self-training is additionally considered in the prediction value stage to compensate for the deficiency in the number of manually labeled gastric polyp images. In this way, the MGNN model can automatically learn the essential features of gastroscopic images without labeling data. The effectiveness of the MGNN model has been verified on real gastroscope images. ELSEVIER 2024-08 Article PeerReviewed Huang, Junjun and Saw, Shier Nee and He, Tianran and Feng, Wei and Loo, Chu Kiong (2024) A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0. JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 34. ISSN 2452-414X, DOI https://doi.org/10.1016/j.jii.2023.100467 <https://doi.org/10.1016/j.jii.2023.100467>. https://doi.org/10.1016/j.jii.2023.100467 10.1016/j.jii.2023.100467
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Huang, Junjun Saw, Shier Nee He, Tianran Feng, Wei Loo, Chu Kiong A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0
description	The emergence of Healthcare 4.0 brings convenience to the diagnosis of gastric polyps patients. The computer aided gastric polyp detection model can automatically locate the position of gastric polyps in gastroscopic images, which helps endoscopists to detect gastric polyps in time and reduce the rate of missed diagnosis. The deep learning model has achieved remarkable success in the field of gastroscopic images, however, it still has the following problems to be solved. Firstly, the model based on the convolutional neural network only analyzes the underlying pixels of the gastroscopic image to locate the polyp, which does not take into account the spatial and positional information contained in the anatomical structure of the gastroscopic image. Secondly, although the number of gastroscopic images is huge, the number of manually annotated gastric polyp images is very small, which makes the deep learning model prone to overfitting. Therefore, in this work, we propose a masked graph neural network model (MGNN) for real-time detecting the location of polyps in gastroscopic images in the Healthcare 4.0. The MGNN model novelly utilizes the graph structure and graph convolution operations to extract spatial location information and semantic information of the gastroscopic images. The information from masked self-training is additionally considered in the prediction value stage to compensate for the deficiency in the number of manually labeled gastric polyp images. In this way, the MGNN model can automatically learn the essential features of gastroscopic images without labeling data. The effectiveness of the MGNN model has been verified on real gastroscope images.
format	Article
author	Huang, Junjun Saw, Shier Nee He, Tianran Feng, Wei Loo, Chu Kiong
author_facet	Huang, Junjun Saw, Shier Nee He, Tianran Feng, Wei Loo, Chu Kiong
author_sort	Huang, Junjun
title	A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0
title_short	A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0
title_full	A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0
title_fullStr	A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0
title_full_unstemmed	A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0
title_sort	masked graph neural network model for real-time gastric polyp detection in healthcare 4.0
publisher	ELSEVIER
publishDate	2024
url	http://eprints.um.edu.my/44155/ https://doi.org/10.1016/j.jii.2023.100467
_version_	1805881125798674432
score	13.211869

A masked graph neural network model for real-time gastric polyp detection in Healthcare 4.0

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