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Graph Neural Network for representation learning of lung cancer

The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings...

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Autores principales: Aftab, Rukhma, Qiang, Yan, Zhao, Juanjuan, Urrehman, Zia, Zhao, Zijuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601264/
https://www.ncbi.nlm.nih.gov/pubmed/37884929
http://dx.doi.org/10.1186/s12885-023-11516-8
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author Aftab, Rukhma
Qiang, Yan
Zhao, Juanjuan
Urrehman, Zia
Zhao, Zijuan
author_facet Aftab, Rukhma
Qiang, Yan
Zhao, Juanjuan
Urrehman, Zia
Zhao, Zijuan
author_sort Aftab, Rukhma
collection PubMed
description The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings. Here, we examine a graph-based model to facilitate end-to-end learning and sample suitable patches using a tile-based approach. We propose MIL-GNN to employ a graph-based Variational Auto-encoder with a Gaussian mixture model to discover relations between sample patches for the purposes to aggregate patch details into an individual vector representation. Using the classical MIL dataset MUSK and distinguishing two lung cancer sub-types, lung cancer called adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we exhibit the efficacy of our technique. We achieved a 97.42% accuracy on the MUSK dataset and a 94.3% AUC on the classification of lung cancer sub-types utilizing features.
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spelling pubmed-106012642023-10-27 Graph Neural Network for representation learning of lung cancer Aftab, Rukhma Qiang, Yan Zhao, Juanjuan Urrehman, Zia Zhao, Zijuan BMC Cancer Research The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings. Here, we examine a graph-based model to facilitate end-to-end learning and sample suitable patches using a tile-based approach. We propose MIL-GNN to employ a graph-based Variational Auto-encoder with a Gaussian mixture model to discover relations between sample patches for the purposes to aggregate patch details into an individual vector representation. Using the classical MIL dataset MUSK and distinguishing two lung cancer sub-types, lung cancer called adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we exhibit the efficacy of our technique. We achieved a 97.42% accuracy on the MUSK dataset and a 94.3% AUC on the classification of lung cancer sub-types utilizing features. BioMed Central 2023-10-26 /pmc/articles/PMC10601264/ /pubmed/37884929 http://dx.doi.org/10.1186/s12885-023-11516-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Aftab, Rukhma
Qiang, Yan
Zhao, Juanjuan
Urrehman, Zia
Zhao, Zijuan
Graph Neural Network for representation learning of lung cancer
title Graph Neural Network for representation learning of lung cancer
title_full Graph Neural Network for representation learning of lung cancer
title_fullStr Graph Neural Network for representation learning of lung cancer
title_full_unstemmed Graph Neural Network for representation learning of lung cancer
title_short Graph Neural Network for representation learning of lung cancer
title_sort graph neural network for representation learning of lung cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601264/
https://www.ncbi.nlm.nih.gov/pubmed/37884929
http://dx.doi.org/10.1186/s12885-023-11516-8
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