Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/132624 |
Resumo: | Cancer is one of the leading death causes in the world, specifically, lung cancer. According to theWorld Health Organization (WHO), at the end of 2020, around 2.2 million people were diagnosedwith lung cancer, and 1.8 million fatalities resulted from it. Correctly identifying it's presence in apatient and classifying it's sub-type and stage is fundamental for the adoption of appropriate targettherapies. One of the gold standards used to identify and classify cancer is the microscopic visual in-spection of histopathological imagesi.e.small tissue samples excised from a patient. Expertpathologists are responsible for this inspection, however, it requires a significant amount of timeand sometimes leads to non-consensual results . With the growth of computational power and data availability, modern Artificial Intelligencesolutions can be developed to automate and speed up this process. Deep Neural Networks us-ing histopathological images as an input currently embody the state-of-the-art in automated lungcancer diagnostic solutions, with Deep Convolutional Neural Networks achieving the most com-pelling acuracies in tissue type classification. One of the main reasons for such results is theincreasing availability of voluminous amounts of data, acquired through the efforts employed byextensive projects like The Cancer Genome Atlas. Nonetheless, histopathological images remain weakly labelled/annotated, as most commonpathologist annotations refer to the entirety of the image and not to individual regions of interestin the patient's tissue sample. Recent works have demonstrated Multiple Instance Learning as asuccessful approach in classification tasks entangled with this lack of annotation, by representingimages as a bag of instances where a single label is available for the whole bag. Thus, we propose a bag/embedding-level lung tissue type and sub-type classifier using a Con-volutional Neural Network in a Multiple Instance Learning approach, where the automated inspec-tion of lung histopathological images determines the presence of cancer, and it's possible sub-type,in a given patient. Furthermore, we employ a post-model interpretability algorithm to validate ourmodel's predictions and highlight the regions of interest for such predictions. |
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Deep Learning Solutions for Lung Cancer Characterization in Histopathological ImagesEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringCancer is one of the leading death causes in the world, specifically, lung cancer. According to theWorld Health Organization (WHO), at the end of 2020, around 2.2 million people were diagnosedwith lung cancer, and 1.8 million fatalities resulted from it. Correctly identifying it's presence in apatient and classifying it's sub-type and stage is fundamental for the adoption of appropriate targettherapies. One of the gold standards used to identify and classify cancer is the microscopic visual in-spection of histopathological imagesi.e.small tissue samples excised from a patient. Expertpathologists are responsible for this inspection, however, it requires a significant amount of timeand sometimes leads to non-consensual results . With the growth of computational power and data availability, modern Artificial Intelligencesolutions can be developed to automate and speed up this process. Deep Neural Networks us-ing histopathological images as an input currently embody the state-of-the-art in automated lungcancer diagnostic solutions, with Deep Convolutional Neural Networks achieving the most com-pelling acuracies in tissue type classification. One of the main reasons for such results is theincreasing availability of voluminous amounts of data, acquired through the efforts employed byextensive projects like The Cancer Genome Atlas. Nonetheless, histopathological images remain weakly labelled/annotated, as most commonpathologist annotations refer to the entirety of the image and not to individual regions of interestin the patient's tissue sample. Recent works have demonstrated Multiple Instance Learning as asuccessful approach in classification tasks entangled with this lack of annotation, by representingimages as a bag of instances where a single label is available for the whole bag. Thus, we propose a bag/embedding-level lung tissue type and sub-type classifier using a Con-volutional Neural Network in a Multiple Instance Learning approach, where the automated inspec-tion of lung histopathological images determines the presence of cancer, and it's possible sub-type,in a given patient. Furthermore, we employ a post-model interpretability algorithm to validate ourmodel's predictions and highlight the regions of interest for such predictions.2020-02-102020-02-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/132624TID:202821366engJoão Moranguinho Bastardo Mourainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T14:07:07Zoai:repositorio-aberto.up.pt:10216/132624Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:55:16.224111Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images |
title |
Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images |
spellingShingle |
Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images João Moranguinho Bastardo Moura Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
title_short |
Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images |
title_full |
Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images |
title_fullStr |
Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images |
title_full_unstemmed |
Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images |
title_sort |
Deep Learning Solutions for Lung Cancer Characterization in Histopathological Images |
author |
João Moranguinho Bastardo Moura |
author_facet |
João Moranguinho Bastardo Moura |
author_role |
author |
dc.contributor.author.fl_str_mv |
João Moranguinho Bastardo Moura |
dc.subject.por.fl_str_mv |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
topic |
Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
description |
Cancer is one of the leading death causes in the world, specifically, lung cancer. According to theWorld Health Organization (WHO), at the end of 2020, around 2.2 million people were diagnosedwith lung cancer, and 1.8 million fatalities resulted from it. Correctly identifying it's presence in apatient and classifying it's sub-type and stage is fundamental for the adoption of appropriate targettherapies. One of the gold standards used to identify and classify cancer is the microscopic visual in-spection of histopathological imagesi.e.small tissue samples excised from a patient. Expertpathologists are responsible for this inspection, however, it requires a significant amount of timeand sometimes leads to non-consensual results . With the growth of computational power and data availability, modern Artificial Intelligencesolutions can be developed to automate and speed up this process. Deep Neural Networks us-ing histopathological images as an input currently embody the state-of-the-art in automated lungcancer diagnostic solutions, with Deep Convolutional Neural Networks achieving the most com-pelling acuracies in tissue type classification. One of the main reasons for such results is theincreasing availability of voluminous amounts of data, acquired through the efforts employed byextensive projects like The Cancer Genome Atlas. Nonetheless, histopathological images remain weakly labelled/annotated, as most commonpathologist annotations refer to the entirety of the image and not to individual regions of interestin the patient's tissue sample. Recent works have demonstrated Multiple Instance Learning as asuccessful approach in classification tasks entangled with this lack of annotation, by representingimages as a bag of instances where a single label is available for the whole bag. Thus, we propose a bag/embedding-level lung tissue type and sub-type classifier using a Con-volutional Neural Network in a Multiple Instance Learning approach, where the automated inspec-tion of lung histopathological images determines the presence of cancer, and it's possible sub-type,in a given patient. Furthermore, we employ a post-model interpretability algorithm to validate ourmodel's predictions and highlight the regions of interest for such predictions. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-10 2020-02-10T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/132624 TID:202821366 |
url |
https://hdl.handle.net/10216/132624 |
identifier_str_mv |
TID:202821366 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799135873023868928 |