Regression and multimodal learning to aid diagnosis in ophthalmology and histopathology

ilustraciones, diagramas

Autores:: Toledo Cortés, Santiago

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Regression and multimodal learning to aid diagnosis in ophthalmology and histopathology
dc.title.translated.spa.fl_str_mv	Regresión y aprendizaje multimodal como ayuda al diagnóstico en oftalmologı́a e histopatologı́a
title	Regression and multimodal learning to aid diagnosis in ophthalmology and histopathology
spellingShingle	Regression and multimodal learning to aid diagnosis in ophthalmology and histopathology 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Histopathology Ophthalmology Deep learning Kernel methods Medical image analysis Multimodal learning Ordinal regression Probabilistic models Quantum machine learning Análisis de imágenes médicas Histopatologı́a Métodos de Kernel Oftalmologı́a Aprendizaje profundo Aprendizaje de máquina cuántico Aprendizaje multimodal Modelos probabilı́sticos Regresión ordinal Teoría de las probabilidades Inteligencia artificial Ciencias médicas Probability theory Artificial intelligence Medical sciences
title_short	Regression and multimodal learning to aid diagnosis in ophthalmology and histopathology
title_full	Regression and multimodal learning to aid diagnosis in ophthalmology and histopathology
title_fullStr	Regression and multimodal learning to aid diagnosis in ophthalmology and histopathology
title_full_unstemmed	Regression and multimodal learning to aid diagnosis in ophthalmology and histopathology
title_sort	Regression and multimodal learning to aid diagnosis in ophthalmology and histopathology
dc.creator.fl_str_mv	Toledo Cortés, Santiago
dc.contributor.advisor.spa.fl_str_mv	González Osorio, Fabio Augusto
dc.contributor.author.spa.fl_str_mv	Toledo Cortés, Santiago
dc.contributor.researchgroup.spa.fl_str_mv	Mindlab
dc.contributor.orcid.spa.fl_str_mv	0000-0003-4172-9263
dc.contributor.cvlac.spa.fl_str_mv	0001449836
dc.contributor.scopus.spa.fl_str_mv	57207843310
dc.contributor.researchgate.spa.fl_str_mv	https://www.researchgate.net/profile/Santiago-Toledo-Cortes-2
dc.contributor.googlescholar.spa.fl_str_mv	https://scholar.google.com/citations?user=M7l6jx4AAAAJ&hl=en
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Histopathology Ophthalmology Deep learning Kernel methods Medical image analysis Multimodal learning Ordinal regression Probabilistic models Quantum machine learning Análisis de imágenes médicas Histopatologı́a Métodos de Kernel Oftalmologı́a Aprendizaje profundo Aprendizaje de máquina cuántico Aprendizaje multimodal Modelos probabilı́sticos Regresión ordinal Teoría de las probabilidades Inteligencia artificial Ciencias médicas Probability theory Artificial intelligence Medical sciences
dc.subject.proposal.eng.fl_str_mv	Histopathology Ophthalmology Deep learning Kernel methods Medical image analysis Multimodal learning Ordinal regression Probabilistic models Quantum machine learning Análisis de imágenes médicas
dc.subject.proposal.spa.fl_str_mv	Histopatologı́a Métodos de Kernel Oftalmologı́a Aprendizaje profundo Aprendizaje de máquina cuántico Aprendizaje multimodal Modelos probabilı́sticos Regresión ordinal
dc.subject.unesco.spa.fl_str_mv	Teoría de las probabilidades Inteligencia artificial Ciencias médicas
dc.subject.unesco.eng.fl_str_mv	Probability theory Artificial intelligence Medical sciences
description	ilustraciones, diagramas
publishDate	2023
dc.date.issued.none.fl_str_mv	2023
dc.date.accessioned.none.fl_str_mv	2024-01-16T19:43:16Z
dc.date.available.none.fl_str_mv	2024-01-16T19:43:16Z
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/85336
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.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/85336 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
dc.relation.indexed.spa.fl_str_mv	Bireme
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González Osorio, Fabio Augusto35912f60905ba6e179208c70e6024e80Toledo Cortés, Santiagoaacc1c99e2c7e404d2f99a7a954b57c8Mindlab0000-0003-4172-9263000144983657207843310https://www.researchgate.net/profile/Santiago-Toledo-Cortes-2https://scholar.google.com/citations?user=M7l6jx4AAAAJ&hl=en2024-01-16T19:43:16Z2024-01-16T19:43:16Z2023https://repositorio.unal.edu.co/handle/unal/85336Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasThe main contribution of this thesis is the development of probabilistic machine learning models to support disease diagnosis from medical data sources. We show how a probabilistic approach offers great versatility in exploiting all available information about the target task. Based on the mathematical formalism of quantum mechanics, we develop and apply machine learning models that allow us to handle the flow of information using density matrices in different ways. We develop mechanisms that can naturally encode not only categorical but also ordinal information, and can also merge different data modalities. Furthermore, we show that the proposed models are naturally interpretable, which allows and facilitates their use in sensitive domains such as health applications. In particular, our models are tested in the diagnosis of several eye diseases and prostate cancer. First, we show the effectiveness and benefit of using regression models in the diagnosis of eye diseases of genetic origin. We then demonstrate the importance of including disease grading information and performing discrete regression to improve the performance of the binary diagnosis of diabetic retinopathy and prostate cancer. We show that a probabilistic interpretation of the results provides information on the uncertainty of the models, which can also be used in training processes. Finally, the proposed framework allows us to encode information using kernel functions, which in turn allows us to naturally introduce flexible information fusion mechanisms and thus to address multimodal tasks. Overall, we show that incorporating ordinal and multimodal information using probabilistic kernel-based frameworks allows learning better data representations, which improves the performance of the models and provides them with a higher level of interpretability.La principal contribución de esta tesis es el desarrollo de modelos probabilísticos de aprendizaje de máquina para apoyar el diagnóstico de enfermedades a partir de información médica. Mostramos cómo un enfoque probabilístico ofrece una gran versatilidad al momento de aprovechar toda la información disponible sobre la tarea objetivo. Basándonos en el formalismo matemático de la mecánica cuántica, desarrollamos y aplicamos modelos de aprendizaje que nos permiten manejar el flujo de información utilizando matrices de densidad de diferentes maneras. Desarrollamos mecanismos que pueden codificar de forma natural no sólo información categórica, sino también ordinal, y que también pueden fusionar distintas modalidades de información. Además, demostramos que los modelos propuestos son naturalmente interpretables, lo que permite y facilita su aplicación en dominios sensibles como las aplicaciones médicas. Precisamente, en este trabajo probamos nuestros modelos en tareas específicas de diagnóstico de enfermedades oculares y cáncer de próstata. En primer lugar, mostramos la eficacia y el beneficio de usar modelos de regresión en el diagnóstico de enfermedades oculares de origen genético. A continuación, demostramos la importancia de incluir información sobre el estadio de las enfermedades y realizar una regresión discreta para mejorar el rendimiento del diagnóstico binario de la retinopatía diabética y el cáncer de próstata. Demostramos que la interpretación probabilística de los resultados proporciona información sobre la incertidumbre de los modelos, que puede utilizarse también en los procesos de entrenamiento. Por último, los modelos propuestos nos permiten codificar la información mediante funciones kernel, que a su vez nos permiten introducir de forma natural mecanismos de fusión de información, flexibles y versátiles, y con estos abordar tareas multimodales. En conjunto, demostramos que la incorporación de información ordinal y multimodal mediante modelos probabilísticos basados en funciones de kernel permite aprender mejores representaciones de los datos, lo que mejora el rendimiento de los modelos y les proporciona un mayor nivel de interpretabilidad. (Texto tomado de la fuente).DoctoradoDoctor en IngenieríaSistemas Inteligentesxvi, 123 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasHistopathologyOphthalmologyDeep learningKernel methodsMedical image analysisMultimodal learningOrdinal regressionProbabilistic modelsQuantum machine learningAnálisis de imágenes médicasHistopatologı́aMétodos de KernelOftalmologı́aAprendizaje profundoAprendizaje de máquina cuánticoAprendizaje multimodalModelos probabilı́sticosRegresión ordinalTeoría de las probabilidadesInteligencia artificialCiencias médicasProbability theoryArtificial intelligenceMedical sciencesRegression and multimodal learning to aid diagnosis in ophthalmology and histopathologyRegresión y aprendizaje multimodal como ayuda al diagnóstico en oftalmologı́a e histopatologı́aTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDBiremeTesting for Glaucoma. https://glaucoma.org/learn-about-glaucoma/testing-for-glaucoma/. 2023Abràmoff, Michael D. ; Folk, James C. ; Han, Dennis P. ; Walker, Jonathan D. ; Williams, David F. ; Russell, Stephen R. ; Massin, Pascale ; Cochener, Beatrice ; Gain, Philippe ; Tang, Li ; Lamard, Mathieu ; Moga, Daniela C. ; Quellec, Gwénolé ; Niemeijer, Meindert: Automated analysis of retinal images for detection of referable diabetic retinopathy. 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In: Quantum Engineering 2 (2020), Nr. 1, S. e34BibliotecariosEstudiantesInvestigadoresMaestrosMedios de comunicaciónPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85336/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1032441097.2023.pdf1032441097.2023.pdfTesis de Doctorado en Ingeniería - Sistemas y Computaciónapplication/pdf9858169https://repositorio.unal.edu.co/bitstream/unal/85336/2/1032441097.2023.pdfed38be9cd99a31689159002b50369530MD52THUMBNAIL1032441097.2023.pdf.jpg1032441097.2023.pdf.jpgGenerated Thumbnailimage/jpeg4745https://repositorio.unal.edu.co/bitstream/unal/85336/3/1032441097.2023.pdf.jpgfa0d97ad04818ae542902f58e146b315MD53unal/85336oai:repositorio.unal.edu.co:unal/853362024-01-16 23:03:36.092Repositorio Institucional Universidad Nacional de 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