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Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks

PURPOSE: Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this paper, we developed a method for classifying subtypes of lung adenocarcinoma from pathological images using neural network whose that can evaluate ph...

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Autores principales: Antonio, Victor Andrew A., Ono, Naoaki, Saito, Akira, Sato, Tetsuo, Altaf-Ul-Amin, Md., Kanaya, Shigehiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223755/
https://www.ncbi.nlm.nih.gov/pubmed/30159833
http://dx.doi.org/10.1007/s11548-018-1835-2
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author Antonio, Victor Andrew A.
Ono, Naoaki
Saito, Akira
Sato, Tetsuo
Altaf-Ul-Amin, Md.
Kanaya, Shigehiko
author_facet Antonio, Victor Andrew A.
Ono, Naoaki
Saito, Akira
Sato, Tetsuo
Altaf-Ul-Amin, Md.
Kanaya, Shigehiko
author_sort Antonio, Victor Andrew A.
collection PubMed
description PURPOSE: Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this paper, we developed a method for classifying subtypes of lung adenocarcinoma from pathological images using neural network whose that can evaluate phenotypic features from wider area to consider cellular distributions. METHODS: In order to recognize the types of tumors, we need not only to detail features of cells, but also to incorporate statistical distribution of the different types of cells. Variants of autoencoders as building blocks of pre-trained convolutional layers of neural networks are implemented. A sparse deep autoencoder which minimizes local information entropy on the encoding layer is then proposed and applied to images of size [Formula: see text] . We applied this model for feature extraction from pathological images of lung adenocarcinoma, which is comprised of three transcriptome subtypes previously defined by the Cancer Genome Atlas network. Since the tumor tissue is composed of heterogeneous cell populations, recognition of tumor transcriptome subtypes requires more information than local pattern of cells. The parameters extracted using this approach will then be used in multiple reduction stages to perform classification on larger images. RESULTS: We were able to demonstrate that these networks successfully recognize morphological features of lung adenocarcinoma. We also performed classification and reconstruction experiments to compare the outputs of the variants. The results showed that the larger input image that covers a certain area of the tissue is required to recognize transcriptome subtypes. The sparse autoencoder network with [Formula: see text] input provides a 98.9% classification accuracy. CONCLUSION: This study shows the potential of autoencoders as a feature extraction paradigm and paves the way for a whole slide image analysis tool to predict molecular subtypes of tumors from pathological features.
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spelling pubmed-62237552018-11-18 Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks Antonio, Victor Andrew A. Ono, Naoaki Saito, Akira Sato, Tetsuo Altaf-Ul-Amin, Md. Kanaya, Shigehiko Int J Comput Assist Radiol Surg Original Article PURPOSE: Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this paper, we developed a method for classifying subtypes of lung adenocarcinoma from pathological images using neural network whose that can evaluate phenotypic features from wider area to consider cellular distributions. METHODS: In order to recognize the types of tumors, we need not only to detail features of cells, but also to incorporate statistical distribution of the different types of cells. Variants of autoencoders as building blocks of pre-trained convolutional layers of neural networks are implemented. A sparse deep autoencoder which minimizes local information entropy on the encoding layer is then proposed and applied to images of size [Formula: see text] . We applied this model for feature extraction from pathological images of lung adenocarcinoma, which is comprised of three transcriptome subtypes previously defined by the Cancer Genome Atlas network. Since the tumor tissue is composed of heterogeneous cell populations, recognition of tumor transcriptome subtypes requires more information than local pattern of cells. The parameters extracted using this approach will then be used in multiple reduction stages to perform classification on larger images. RESULTS: We were able to demonstrate that these networks successfully recognize morphological features of lung adenocarcinoma. We also performed classification and reconstruction experiments to compare the outputs of the variants. The results showed that the larger input image that covers a certain area of the tissue is required to recognize transcriptome subtypes. The sparse autoencoder network with [Formula: see text] input provides a 98.9% classification accuracy. CONCLUSION: This study shows the potential of autoencoders as a feature extraction paradigm and paves the way for a whole slide image analysis tool to predict molecular subtypes of tumors from pathological features. Springer International Publishing 2018-08-29 2018 /pmc/articles/PMC6223755/ /pubmed/30159833 http://dx.doi.org/10.1007/s11548-018-1835-2 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Antonio, Victor Andrew A.
Ono, Naoaki
Saito, Akira
Sato, Tetsuo
Altaf-Ul-Amin, Md.
Kanaya, Shigehiko
Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks
title Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks
title_full Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks
title_fullStr Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks
title_full_unstemmed Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks
title_short Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks
title_sort classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223755/
https://www.ncbi.nlm.nih.gov/pubmed/30159833
http://dx.doi.org/10.1007/s11548-018-1835-2
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