Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification

Detalhes bibliográficos
Autor(a) principal: André L. S. Meirelles
Data de Publicação: 2022
Outros Autores: Tahsin Kurc, Jun Kong, Renato Antonio Celso Ferreira, Joel H. Saltz, George Teodoro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: https://doi.org/10.3389/fmed.2022.894430
http://hdl.handle.net/1843/61019
https://orcid.org/0000-0002-4372-8996
Resumo: CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
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spelling 2023-11-16T21:23:05Z2023-11-16T21:23:05Z2022-05-31989443010https://doi.org/10.3389/fmed.2022.8944302296-858Xhttp://hdl.handle.net/1843/61019https://orcid.org/0000-0002-4372-8996CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas GeraisCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorBackground: Deep learning methods have demonstrated remarkable performance in pathology image analysis, but they are computationally very demanding. The aim of our study is to reduce their computational cost to enable their use with large tissue image datasets. Methods: We propose a method called Network Auto-Reduction (NAR) that simplifies a Convolutional Neural Network (CNN) by reducing the network to minimize the computational cost of doing a prediction. NAR performs a compound scaling in which the width, depth, and resolution dimensions of the network are reduced together to maintain a balance among them in the resulting simplified network. We compare our method with a state-of-the-art solution called ResRep. The evaluation is carried out with popular CNN architectures and a real-world application that identifies distributions of tumor-infiltrating lymphocytes in tissue images. Results: The experimental results show that both ResRep and NAR are able to generate simplified, more efficient versions of ResNet50 V2. The simplified versions by ResRep and NAR require 1.32× and 3.26× fewer floating-point operations (FLOPs), respectively, than the original network without a loss in classification power as measured by the Area under the Curve (AUC) metric. When applied to a deeper and more computationally expensive network, Inception V4, NAR is able to generate a version that requires 4× lower than the original version with the same AUC performance. Conclusions: NAR is able to achieve substantial reductions in the execution cost of two popular CNN architectures, while resulting in small or no loss in model accuracy. Such cost savings can significantly improve the use of deep learning methods in digital pathology. They can enable studies with larger tissue image datasets and facilitate the use of less expensive and more accessible graphics processing units (GPUs), thus reducing the computing costs of a study.engUniversidade Federal de Minas GeraisUFMGBrasilICEX - INSTITUTO DE CIÊNCIAS EXATASICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃOFrontiers in MedicineApendizado do computadorTumoresLinfócitosAprendizado profundo (Aprendizado do computador)Digital pathologyDeep learningCNN simplificationTumor-infiltrating lymphocytesEfficient CNNsBuilding Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classificationinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://www.frontiersin.org/articles/10.3389/fmed.2022.894430/fullAndré L. S. MeirellesTahsin KurcJun KongRenato Antonio Celso FerreiraJoel H. SaltzGeorge Teodoroapplication/pdfinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/61019/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALBuilding Efficient CNN Architectures for Histopathology Images Analysis_ A Case-Study in Tumor-Infiltrating Lymphocytes Classification.pdfBuilding Efficient CNN Architectures for Histopathology Images Analysis_ A Case-Study in Tumor-Infiltrating Lymphocytes Classification.pdfapplication/pdf663732https://repositorio.ufmg.br/bitstream/1843/61019/2/Building%20Efficient%20CNN%20Architectures%20for%20Histopathology%20Images%20Analysis_%20A%20Case-Study%20in%20Tumor-Infiltrating%20Lymphocytes%20Classification.pdfd2ea5561405c11404565731066337a3bMD521843/610192023-11-16 18:23:05.609oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-11-16T21:23:05Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification
title Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification
spellingShingle Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification
André L. S. Meirelles
Digital pathology
Deep learning
CNN simplification
Tumor-infiltrating lymphocytes
Efficient CNNs
Apendizado do computador
Tumores
Linfócitos
Aprendizado profundo (Aprendizado do computador)
title_short Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification
title_full Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification
title_fullStr Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification
title_full_unstemmed Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification
title_sort Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification
author André L. S. Meirelles
author_facet André L. S. Meirelles
Tahsin Kurc
Jun Kong
Renato Antonio Celso Ferreira
Joel H. Saltz
George Teodoro
author_role author
author2 Tahsin Kurc
Jun Kong
Renato Antonio Celso Ferreira
Joel H. Saltz
George Teodoro
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv André L. S. Meirelles
Tahsin Kurc
Jun Kong
Renato Antonio Celso Ferreira
Joel H. Saltz
George Teodoro
dc.subject.por.fl_str_mv Digital pathology
Deep learning
CNN simplification
Tumor-infiltrating lymphocytes
Efficient CNNs
topic Digital pathology
Deep learning
CNN simplification
Tumor-infiltrating lymphocytes
Efficient CNNs
Apendizado do computador
Tumores
Linfócitos
Aprendizado profundo (Aprendizado do computador)
dc.subject.other.pt_BR.fl_str_mv Apendizado do computador
Tumores
Linfócitos
Aprendizado profundo (Aprendizado do computador)
description CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
publishDate 2022
dc.date.issued.fl_str_mv 2022-05-31
dc.date.accessioned.fl_str_mv 2023-11-16T21:23:05Z
dc.date.available.fl_str_mv 2023-11-16T21:23:05Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1843/61019
dc.identifier.doi.pt_BR.fl_str_mv https://doi.org/10.3389/fmed.2022.894430
dc.identifier.issn.pt_BR.fl_str_mv 2296-858X
dc.identifier.orcid.pt_BR.fl_str_mv https://orcid.org/0000-0002-4372-8996
url https://doi.org/10.3389/fmed.2022.894430
http://hdl.handle.net/1843/61019
https://orcid.org/0000-0002-4372-8996
identifier_str_mv 2296-858X
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.pt_BR.fl_str_mv Frontiers in Medicine
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv ICEX - INSTITUTO DE CIÊNCIAS EXATAS
ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
bitstream.url.fl_str_mv https://repositorio.ufmg.br/bitstream/1843/61019/1/License.txt
https://repositorio.ufmg.br/bitstream/1843/61019/2/Building%20Efficient%20CNN%20Architectures%20for%20Histopathology%20Images%20Analysis_%20A%20Case-Study%20in%20Tumor-Infiltrating%20Lymphocytes%20Classification.pdf
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