Immunohistochemistry images analysis with convolutional and graph neural networks

Breast cancer is one of the most prevalent diseases in our society. The survival rate is increasing thanks to medical advances and the early detection of the disease through various diagnostic tests. To achieve this, a tissue sample is extracted from a biopsy, stained with different immunohistochemi...

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Detalles Bibliográficos
Autor: Rabanaque Rodríguez, Sonia
Tipo de recurso: tesis de maestría
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/406217
Acceso en línea:https://hdl.handle.net/2117/406217
Access Level:acceso abierto
Palabra clave:Neural networks (Computer science)
Artificial intelligence
Histologia
càncer de mama
xarxes neuronals convolucionals
xarxes neuronals basades en grafs
Histology
Breast cancer
Convolutional Neural Networks
Graph Neural Networks
Xarxes neuronals (Informàtica)
Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
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oai_identifier_str oai:upcommons.upc.edu:2117/406217
network_acronym_str ES
network_name_str España
repository_id_str
spelling Immunohistochemistry images analysis with convolutional and graph neural networksRabanaque Rodríguez, SoniaNeural networks (Computer science)Artificial intelligenceHistologiacàncer de mamaxarxes neuronals convolucionalsxarxes neuronals basades en grafsHistologyBreast cancerConvolutional Neural NetworksGraph Neural NetworksXarxes neuronals (Informàtica)Intel·ligència artificialÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialBreast cancer is one of the most prevalent diseases in our society. The survival rate is increasing thanks to medical advances and the early detection of the disease through various diagnostic tests. To achieve this, a tissue sample is extracted from a biopsy, stained with different immunohistochemical techniques and analysed to quantify the tumour cells. For instance, the Estrogen Receptor (ER), Progesterone Receptor (PR), and Ki67 are some of the most commonly used stains to identify tumour grade. This work is developed within the DigiPatICS project, which aims to provide algorithms for the Institut Català de la Salut (ICS). Our objective is to develop a method to segment and classify cells present in breast tissue to obtain meaningful indicators that will support pathologists in the diagnosis of tumour. The proposed method uses a Convolutional Neural Network (specifically, the Hover-Net) followed by a Graph Neural Network (GNN) to analyse these immunohistochemical stains. In the first stage, the cells are segmented and classified, and then the resulting prediction is used to construct a graph. This information is then processed by the GNN to obtain the final classification of the identified instances. The results indicate that the proposed method helps to classify the cells in the three analysed stains: ER, PR, and Ki67. We studied the effect of several factors of the method, including the Hover-Net and the Graph Neural Network, as well as the features used to represent each segmented instance and in the conditions to generate the graph. In addition, we have interpreted the results and identified significant aspects, such as the influence of certain nodes in classifying their neighbours in some situations. This information is provided by the attention weights assigned to each pair of neighbours by certain layers used in the GNN.Universitat Politècnica de CatalunyaSalembier Clairon, Philippe JeanCasas, Josep Ramon20242024-01-2220242024-04-09master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/406217reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4062172026-05-27T15:37:01Z
dc.title.none.fl_str_mv Immunohistochemistry images analysis with convolutional and graph neural networks
title Immunohistochemistry images analysis with convolutional and graph neural networks
spellingShingle Immunohistochemistry images analysis with convolutional and graph neural networks
Rabanaque Rodríguez, Sonia
Neural networks (Computer science)
Artificial intelligence
Histologia
càncer de mama
xarxes neuronals convolucionals
xarxes neuronals basades en grafs
Histology
Breast cancer
Convolutional Neural Networks
Graph Neural Networks
Xarxes neuronals (Informàtica)
Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Immunohistochemistry images analysis with convolutional and graph neural networks
title_full Immunohistochemistry images analysis with convolutional and graph neural networks
title_fullStr Immunohistochemistry images analysis with convolutional and graph neural networks
title_full_unstemmed Immunohistochemistry images analysis with convolutional and graph neural networks
title_sort Immunohistochemistry images analysis with convolutional and graph neural networks
dc.creator.none.fl_str_mv Rabanaque Rodríguez, Sonia
author Rabanaque Rodríguez, Sonia
author_facet Rabanaque Rodríguez, Sonia
author_role author
dc.contributor.none.fl_str_mv Salembier Clairon, Philippe Jean
Casas, Josep Ramon
dc.subject.none.fl_str_mv Neural networks (Computer science)
Artificial intelligence
Histologia
càncer de mama
xarxes neuronals convolucionals
xarxes neuronals basades en grafs
Histology
Breast cancer
Convolutional Neural Networks
Graph Neural Networks
Xarxes neuronals (Informàtica)
Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Neural networks (Computer science)
Artificial intelligence
Histologia
càncer de mama
xarxes neuronals convolucionals
xarxes neuronals basades en grafs
Histology
Breast cancer
Convolutional Neural Networks
Graph Neural Networks
Xarxes neuronals (Informàtica)
Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description Breast cancer is one of the most prevalent diseases in our society. The survival rate is increasing thanks to medical advances and the early detection of the disease through various diagnostic tests. To achieve this, a tissue sample is extracted from a biopsy, stained with different immunohistochemical techniques and analysed to quantify the tumour cells. For instance, the Estrogen Receptor (ER), Progesterone Receptor (PR), and Ki67 are some of the most commonly used stains to identify tumour grade. This work is developed within the DigiPatICS project, which aims to provide algorithms for the Institut Català de la Salut (ICS). Our objective is to develop a method to segment and classify cells present in breast tissue to obtain meaningful indicators that will support pathologists in the diagnosis of tumour. The proposed method uses a Convolutional Neural Network (specifically, the Hover-Net) followed by a Graph Neural Network (GNN) to analyse these immunohistochemical stains. In the first stage, the cells are segmented and classified, and then the resulting prediction is used to construct a graph. This information is then processed by the GNN to obtain the final classification of the identified instances. The results indicate that the proposed method helps to classify the cells in the three analysed stains: ER, PR, and Ki67. We studied the effect of several factors of the method, including the Hover-Net and the Graph Neural Network, as well as the features used to represent each segmented instance and in the conditions to generate the graph. In addition, we have interpreted the results and identified significant aspects, such as the influence of certain nodes in classifying their neighbours in some situations. This information is provided by the attention weights assigned to each pair of neighbours by certain layers used in the GNN.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-22
2024
2024-04-09
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/406217
url https://hdl.handle.net/2117/406217
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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