A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images

ilustraciones, diagramas

Autores:
Cano Ramirez, Fabian Alberto
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/84587
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/84587
https://repositorio.unal.edu.co/
Palabra clave:
610 - Medicina y salud
000 - Ciencias de la computación, información y obras generales
620 - Ingeniería y operaciones afines
Patología clínica
Tejidos
Procesamiento de imagen asistido por computador
Image Processing, Computer-Assisted
Pathology, Clinical
Tissues
Digital Pathology
Tissue Representation
Histopathology
Variational Autoencoder
Lung Adenocarcinoma
Lung Cancer
Patología Digital
Representación de tejidos
Histopatología
Autocodificador Variaciona
Adenocarcinoma de pulmón
Cáncer de pulmón
Rights
openAccess
License
Reconocimiento 4.0 Internacional
id UNACIONAL2_e6c535fc3cd404612778081223947dc9
oai_identifier_str oai:repositorio.unal.edu.co:unal/84587
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images
dc.title.translated.spa.fl_str_mv Un aprendizaje de representación basado en datos para la diferenciación de tejido tumoral a partir de imágenes de histopatología de cáncer de pulmón de células no pequeñas
title A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images
spellingShingle A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images
610 - Medicina y salud
000 - Ciencias de la computación, información y obras generales
620 - Ingeniería y operaciones afines
Patología clínica
Tejidos
Procesamiento de imagen asistido por computador
Image Processing, Computer-Assisted
Pathology, Clinical
Tissues
Digital Pathology
Tissue Representation
Histopathology
Variational Autoencoder
Lung Adenocarcinoma
Lung Cancer
Patología Digital
Representación de tejidos
Histopatología
Autocodificador Variaciona
Adenocarcinoma de pulmón
Cáncer de pulmón
title_short A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images
title_full A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images
title_fullStr A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images
title_full_unstemmed A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images
title_sort A Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology Images
dc.creator.fl_str_mv Cano Ramirez, Fabian Alberto
dc.contributor.advisor.none.fl_str_mv Romero Castro, Eduardo
Cruz Roa, Angel Alfonso
dc.contributor.author.none.fl_str_mv Cano Ramirez, Fabian Alberto
dc.contributor.researchgroup.spa.fl_str_mv Cim@Lab
dc.contributor.orcid.spa.fl_str_mv Cano, Fabian [0000-0003-3272-8701]
dc.contributor.cvlac.spa.fl_str_mv Cano, Fabian [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000183272]
dc.subject.ddc.spa.fl_str_mv 610 - Medicina y salud
000 - Ciencias de la computación, información y obras generales
620 - Ingeniería y operaciones afines
topic 610 - Medicina y salud
000 - Ciencias de la computación, información y obras generales
620 - Ingeniería y operaciones afines
Patología clínica
Tejidos
Procesamiento de imagen asistido por computador
Image Processing, Computer-Assisted
Pathology, Clinical
Tissues
Digital Pathology
Tissue Representation
Histopathology
Variational Autoencoder
Lung Adenocarcinoma
Lung Cancer
Patología Digital
Representación de tejidos
Histopatología
Autocodificador Variaciona
Adenocarcinoma de pulmón
Cáncer de pulmón
dc.subject.decs.spa.fl_str_mv Patología clínica
Tejidos
Procesamiento de imagen asistido por computador
Image Processing, Computer-Assisted
dc.subject.decs.eng.fl_str_mv Pathology, Clinical
Tissues
dc.subject.proposal.eng.fl_str_mv Digital Pathology
Tissue Representation
Histopathology
Variational Autoencoder
Lung Adenocarcinoma
Lung Cancer
dc.subject.proposal.spa.fl_str_mv Patología Digital
Representación de tejidos
Histopatología
Autocodificador Variaciona
Adenocarcinoma de pulmón
Cáncer de pulmón
description ilustraciones, diagramas
publishDate 2022
dc.date.issued.none.fl_str_mv 2022
dc.date.accessioned.none.fl_str_mv 2023-08-23T13:59:59Z
dc.date.available.none.fl_str_mv 2023-08-23T13:59:59Z
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/84587
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.repo.none.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/84587
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
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spelling Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Romero Castro, Eduardocdde36df751c8bac46785c53d50fefcaCruz Roa, Angel Alfonsofc07088f5c43fb7bffba9a880fb78a24Cano Ramirez, Fabian Albertoa3b8895e1ba428f249547079198ecbd3Cim@LabCano, Fabian [0000-0003-3272-8701]Cano, Fabian [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000183272]2023-08-23T13:59:59Z2023-08-23T13:59:59Z2022https://repositorio.unal.edu.co/handle/unal/84587Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasLung cancer is the second most common type and the leading cause of cancer death in the world. It is divided into different types according to cellular and tissular features, and in turn, these types are distinguished by typical patterns that represent them. Each histological subtype of lung cancer is associated with the prognosis and treatment of patients, and is subjectively stratified mainly by its morphological features. However, due to the very nature of the disease, this stratification varies since there is no specialized grading system, and also because of the difficulty of characterizing cases that generally contain mixtures of histological patterns and unspecified tissues, which therefore, alters the diagnosis and prognosis of patients. This research work addresses a computational data-driven strategy to characterize histological patterns of lung cancer, in addition to determining its differentiation and aggressiveness, in order to support decision-making in clinical practice. Therefore, this work has been divided in two parts. The first part presents a supervised subtype differentiation learning of lung cancer features in a latent space constructed with a variational autoencoder. In such space, complicated patterns are quantified by estimating a differentiation grade of typical encoded features of lung cancer subtypes. Then, a logistic regression model assigns differentiation cancer subtype grade to the embedded tissue samples. This approach builds up a subtype differentiation grade of non-small cell lung cancer among complex structures which are fully interpretable and integrable with a pathology workflow. Finally, the second part presents an unsupervised computational approach based on an ensemble of tissue-specialized variational autoencoders, which were trained per histopathology subtype, to build an unsupervised embedded tissue-image representation. This representation was used to train a Random Forest classifier of three lung adenocarcinoma histology subtypes (lepidic, papillary and solid), and a 2D-visually interpretable projection from the learned embedded representation. (Texto tomado de la fuente)El cáncer de pulmón es el segundo tipo más común y la principal causa de muerte por cáncer en el mundo. Se divide en diferentes tipos según las características celulares y tisulares, y a su vez, estos tipos se distinguen por los patrones histológicos típicos que los representan. Cada subtipo histológico de cáncer de pulmón se asocia con el pronóstico y tratamiento de los pacientes, y se estratifica subjetivamente por parte de los patólogos principalmente por sus características morfológicas. Sin embargo, por la propia naturaleza de la enfermedad, esta estratificación varía ya que no existe un sistema de gradación especializado, y también por la dificultad de caracterizar los casos que generalmente contienen mezclas de patrones histológicos y tejidos no especificados, lo que puede afectar la precisión del diagnóstico y pronóstico de los pacientes. Este trabajo de investigación aborda una estrategia computacional basada en datos para caracterizar los patrones histológicos del cáncer de pulmón, además de determinar su diferenciación y agresividad, con el fin de apoyar la toma de decisiones en la práctica clínica. Por ello, este trabajo se ha dividido en dos partes. La primera parte presenta un aprendizaje supervisado de diferenciación de subtipos de características de cáncer de pulmón en un espacio latente construido con un autocodificador variacional. En dicho espacio, los patrones complejos se cuantifican mediante la estimación de un grado de diferenciación de las características codificadas típicas de los subtipos de cáncer de pulmón. Luego, un modelo de regresión logística asigna un grado de diferenciación del subtipo de cáncer a las muestras de tejido codificadas. Este enfoque construye un grado de diferenciación de subtipos de cáncer de pulmón de células no pequeñas entre estructuras complejas que son totalmente interpretables e integrables con un flujo de trabajo de patología. Finalmente, la segunda parte presenta un enfoque computacional no supervisado basado en un conjunto de codificadores automáticos variacionales especializados en tejidos, que fueron entrenados por subtipo de histopatología, para construir una representación de imagen de tejido codificada no supervisada. Esta representación se usó para entrenar un clasificador Random Forest para distinguir entre tres subtipos histológicos de adenocarcinoma de pulmón (lepídico, papilar y sólido) y una proyección visualmente interpretable en 2D a partir de la representación incrustada aprendida.MaestríaMagíster en Ingeniería BiomédicaIngeniería Biomédicaxv, 46 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Medicina - Maestría en Ingeniería BiomédicaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud000 - Ciencias de la computación, información y obras generales620 - Ingeniería y operaciones afinesPatología clínicaTejidosProcesamiento de imagen asistido por computadorImage Processing, Computer-AssistedPathology, ClinicalTissuesDigital PathologyTissue RepresentationHistopathologyVariational AutoencoderLung AdenocarcinomaLung CancerPatología DigitalRepresentación de tejidosHistopatologíaAutocodificador VariacionaAdenocarcinoma de pulmónCáncer de pulmónA Data-driven Representation Learning for Tumor Tissue Differentiation from Non-Small Cell Lung Cancer Histopathology ImagesUn aprendizaje de representación basado en datos para la diferenciación de tejido tumoral a partir de imágenes de histopatología de cáncer de pulmón de células no pequeñasTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMV. 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McAuliffe, “Variational inference: A review for statisticians.,” Journal of the American Statistical Association, vol. 112, no. 518, pp. 859–877, 2017.EstudiantesORIGINAL1121932413.2022.pdf1121932413.2022.pdfTesis de Maestría en Ingeniería Biomédicaapplication/pdf6787478https://repositorio.unal.edu.co/bitstream/unal/84587/2/1121932413.2022.pdf17c882ea5d1cd9eb74c8dc105392baabMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84587/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53THUMBNAIL1121932413.2022.pdf.jpg1121932413.2022.pdf.jpgGenerated Thumbnailimage/jpeg4876https://repositorio.unal.edu.co/bitstream/unal/84587/4/1121932413.2022.pdf.jpg14c346d3516f3f62fa8f2e4912c23d8cMD54unal/84587oai:repositorio.unal.edu.co:unal/845872023-08-23 23:03:44.615Repositorio Institucional Universidad Nacional de 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