Semi-supervised deep learning for ocular image classification

ilustraciones, gráficas, tablas

Autores:: Arrieta Ramos, José Miguel

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Semi-supervised deep learning for ocular image classification
dc.title.translated.spa.fl_str_mv	Aprendizaje profundo semi-supervisado para la clasificación de imágenes oculares
title	Semi-supervised deep learning for ocular image classification
spellingShingle	Semi-supervised deep learning for ocular image classification 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Diabetic Retinopathy/diagnostic imaging Deep Learning Machine Learning Retinopatía Diabética/diagnóstico por imagen Aprendizaje Profundo Aprendizaje Automático Self-supervised learning Diabetic retinopathy Medical imaging Deep learning Semi-supervised learning Imágenes médicas Aprendizaje profundo Aprendizaje semi-supervisado Aprendizaje autosupervisado Retinopatía diabética
title_short	Semi-supervised deep learning for ocular image classification
title_full	Semi-supervised deep learning for ocular image classification
title_fullStr	Semi-supervised deep learning for ocular image classification
title_full_unstemmed	Semi-supervised deep learning for ocular image classification
title_sort	Semi-supervised deep learning for ocular image classification
dc.creator.fl_str_mv	Arrieta Ramos, José Miguel
dc.contributor.advisor.spa.fl_str_mv	González Osorio, Fabio Augusto Perdomo Charry, Oscar Julián
dc.contributor.author.spa.fl_str_mv	Arrieta Ramos, José Miguel
dc.contributor.referee.spa.fl_str_mv	Romero Castro, Edgar Eduardo Toledo Cortés, Santiago
dc.contributor.researchgroup.spa.fl_str_mv	Mindlab
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Diabetic Retinopathy/diagnostic imaging Deep Learning Machine Learning Retinopatía Diabética/diagnóstico por imagen Aprendizaje Profundo Aprendizaje Automático Self-supervised learning Diabetic retinopathy Medical imaging Deep learning Semi-supervised learning Imágenes médicas Aprendizaje profundo Aprendizaje semi-supervisado Aprendizaje autosupervisado Retinopatía diabética
dc.subject.decs.eng.fl_str_mv	Diabetic Retinopathy/diagnostic imaging Deep Learning Machine Learning
dc.subject.decs.spa.fl_str_mv	Retinopatía Diabética/diagnóstico por imagen Aprendizaje Profundo Aprendizaje Automático
dc.subject.proposal.eng.fl_str_mv	Self-supervised learning Diabetic retinopathy Medical imaging Deep learning Semi-supervised learning
dc.subject.proposal.spa.fl_str_mv	Imágenes médicas Aprendizaje profundo Aprendizaje semi-supervisado Aprendizaje autosupervisado Retinopatía diabética
description	ilustraciones, gráficas, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-06-21T19:42:01Z
dc.date.available.none.fl_str_mv	2022-06-21T19:42:01Z
dc.date.issued.none.fl_str_mv	2022-06-03
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/81620
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/81620 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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dc.format.extent.spa.fl_str_mv	viii, 35 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
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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_abf2González Osorio, Fabio Augusto0e9d70b5c1d7448338ca4467ccb27e59Perdomo Charry, Oscar Juliánc280ba13fd48e8dbf9cdbc8179aa9c94Arrieta Ramos, José Migueldd7a3994227ded26eb619482604084e5600Romero Castro, Edgar EduardoToledo Cortés, SantiagoMindlab2022-06-21T19:42:01Z2022-06-21T19:42:01Z2022-06-03https://repositorio.unal.edu.co/handle/unal/81620Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasRegular screening, early diagnosis, and appropriate on-time treatment could prevent vision loss and blindness as a complication of diabetes. Unfortunately, access to expert ophthal- mologists is limited and not readily available. Therefore, automated detection systems could improve access to specialized care by reducing screening time, cost, and e↵ort. Deep learning methods became popular for detecting ocular disease on eye fundus images because of their promising results. However, deep learning models need a large number of labeled images to learn, and the manual labeling of medical images results in a time-consuming and expensive process that requires medical experts in the retina, with little time to devote to this task. As a result, a limited number of annotated images are available. This thesis work proposes a semi-supervised method that leverages unlabeled images and labeled ones to train a mo- del that detects diabetic retinopathy via self-supervised pre-training followed by supervised fine-tuning and knowledge distillation with a small set of labeled images. This method was evaluated on the Messidor-2 dataset achieving 0.89 AUC using only 2 % EyePACS-Kaggle train labeled images.La pérdida de visión y ceguera como complicacíon de la diabetes se podrían prevenir con diagnóstico temprano, exámenes de deteccíon frequentes, y tratamiento oportuno adecuado. Desafortunadamente, el acceso a un oftalmólogo experto es limitado y no es fácilmente disponible. Es por esto que los sistemas de detección automatizados podrían mejorar el acceso a la atención especializada al reducir el tiempo, el costo y el esfuerzo para la detección. Los métodos de aprendizaje profundo se hicieron populares para la detección de enfermedades oculares en imágenes de fondo de ojo debido a sus buenos resultados. Sin embargo, los métodos de aprendizaje profundo necesitan una gran cantidad de imágenes etiquetadas para aprender, siendo el etiquetado manual de imágenes médicas un proceso costoso y lento que requiere escasos expertos médicos en la retina. Como resultado, el número de imágenes anotadas disponibles es limitado. Con este trabajo de tesis se propone un método semi-supervisado que aproveche las imágenes no etiquetadas además de las imágenes etiquetadas para entrenar un modelo que detecte la retinopatía diabética a través de aprendizaje auto-supervisado seguido de un ajuste fino supervisado y destilacion de conocimiento. Este método fue evaluado en el dataset de Messidor-2 logrando un AUC de 0.89 usando solamente 2 % de la particion de entrenamiento de EyePACS-Kaggle con imagenes etiquetadas. (Texto tomado de la fuente).MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónIntelligent systemsviii, 35 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresDiabetic Retinopathy/diagnostic imagingDeep LearningMachine LearningRetinopatía Diabética/diagnóstico por imagenAprendizaje ProfundoAprendizaje AutomáticoSelf-supervised learningDiabetic retinopathyMedical imagingDeep learningSemi-supervised learningImágenes médicasAprendizaje profundoAprendizaje semi-supervisadoAprendizaje autosupervisadoRetinopatía diabéticaSemi-supervised deep learning for ocular image classificationAprendizaje profundo semi-supervisado para la clasificación de imágenes ocularesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAlbarqouni, Shadi ; Baur, Christoph ; Achilles, Felix ; Belagiannis, Vasileios ; Demirci, Stefanie ; Navab, Nassir: AggNet: Deep Learning From Crowds for Mito- sis Detection in Breast Cancer Histology Images. 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En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2018), p. 8697–8710. – ISBN 9781538664209ORIGINAL1143362204.2022.pdf1143362204.2022.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf5715380https://repositorio.unal.edu.co/bitstream/unal/81620/3/1143362204.2022.pdf729696ee30585bf4f6f54fb45384503aMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81620/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1143362204.2022.pdf.jpg1143362204.2022.pdf.jpgGenerated Thumbnailimage/jpeg4248https://repositorio.unal.edu.co/bitstream/unal/81620/5/1143362204.2022.pdf.jpg96dd9be5f9f03f0c8fe35903f56226c4MD55unal/81620oai:repositorio.unal.edu.co:unal/816202023-08-05 23:04:01.885Repositorio Institucional Universidad Nacional de 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Semi-supervised deep learning for ocular image classification

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