A Deep Learning model for automatic grading of prostate cancer histopathology images

Gleason grading is recognized as the standard method for diagnosing prostate cancer. However, it is subject to significant inter-observer variability due to its reliance on subjective visual assessment. Current deep learning approaches for grading often require exhaustive pixel-level annotations and...

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Autores:: Medina Carrillo, Sebastian Rodrigo

Tipo de recurso:

Fecha de publicación:: 2024

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
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dc.title.eng.fl_str_mv	A Deep Learning model for automatic grading of prostate cancer histopathology images
dc.title.translated.spa.fl_str_mv	Modelo de Deep Learning para la gradación automática de imágenes histopatológicas de cáncer de próstata
title	A Deep Learning model for automatic grading of prostate cancer histopathology images
spellingShingle	A Deep Learning model for automatic grading of prostate cancer histopathology images 610 - Medicina y salud::616 - Enfermedades 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Aprendizaje Profundo Neoplasias de la Próstata/diagnóstico por imagen Patología Deep Learning Prostatic Neoplasms/diagnostic imaging Pathology Prostate cancer Histopathology Deep Learning Cancer grading Density matrix Interpretability Cáncer de prostata Histopatología Aprendizaje automático Gradación de cáncer Matriz de densidad Interpretabilidad
title_short	A Deep Learning model for automatic grading of prostate cancer histopathology images
title_full	A Deep Learning model for automatic grading of prostate cancer histopathology images
title_fullStr	A Deep Learning model for automatic grading of prostate cancer histopathology images
title_full_unstemmed	A Deep Learning model for automatic grading of prostate cancer histopathology images
title_sort	A Deep Learning model for automatic grading of prostate cancer histopathology images
dc.creator.fl_str_mv	Medina Carrillo, Sebastian Rodrigo
dc.contributor.advisor.spa.fl_str_mv	González Osorio, Fabio Augusto Cruz Roa, Ángel Alfonso
dc.contributor.author.spa.fl_str_mv	Medina Carrillo, Sebastian Rodrigo
dc.contributor.referee.spa.fl_str_mv	Romero, Eduardo Tabares Soto, Reinel
dc.contributor.researchgroup.spa.fl_str_mv	Mindlab
dc.subject.ddc.spa.fl_str_mv	610 - Medicina y salud::616 - Enfermedades 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	610 - Medicina y salud::616 - Enfermedades 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Aprendizaje Profundo Neoplasias de la Próstata/diagnóstico por imagen Patología Deep Learning Prostatic Neoplasms/diagnostic imaging Pathology Prostate cancer Histopathology Deep Learning Cancer grading Density matrix Interpretability Cáncer de prostata Histopatología Aprendizaje automático Gradación de cáncer Matriz de densidad Interpretabilidad
dc.subject.decs.spa.fl_str_mv	Aprendizaje Profundo Neoplasias de la Próstata/diagnóstico por imagen Patología
dc.subject.decs.eng.fl_str_mv	Deep Learning Prostatic Neoplasms/diagnostic imaging Pathology
dc.subject.proposal.eng.fl_str_mv	Prostate cancer Histopathology Deep Learning Cancer grading Density matrix Interpretability
dc.subject.proposal.spa.fl_str_mv	Cáncer de prostata Histopatología Aprendizaje automático Gradación de cáncer Matriz de densidad Interpretabilidad
description	Gleason grading is recognized as the standard method for diagnosing prostate cancer. However, it is subject to significant inter-observer variability due to its reliance on subjective visual assessment. Current deep learning approaches for grading often require exhaustive pixel-level annotations and are generally limited to patch-level predictions, which do not incorporate slide-level information. Recently, weakly-supervised techniques have shown promise in generating whole-slide label predictions using pathology report labels, which are more readily available. However, these methods frequently lack visual and quantitative interpretability, reinforcing the black box nature of deep learning models, hindering their clinical adoption. This thesis introduces WiSDoM, a novel weakly-supervised and interpretable approach leveraging attention mechanisms and Kernel Density Matrices for the grading of prostate cancer on whole slides. This method is adaptable to varying levels of supervision. WiSDoM facilitates multi-scale interpretability through several features: detailed heatmaps that provide granular visual insights by highlighting critical morphological features without requiring tissue annotations; example-based phenotypical prototypes that illustrate the internal representation learned by the model, aiding in clinical verification; and visual-quantitative measures of model uncertainty, which enhance the transparency of the model's decision-making process, a crucial factor for clinical use. WiSDoM has been validated on core-needle biopsies from two different institutions, demonstrating robust agreement with the reference standard (quadratically weighted Kappa of 0.93). WiSDoM achieves state-of-the-art inter-observer agreement performance on the PANDA Challenge publicly available dataset while being clinically interpretable.
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-17T02:18:49Z
dc.date.available.none.fl_str_mv	2024-07-17T02:18:49Z
dc.date.issued.none.fl_str_mv	2024
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/86504
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/86504 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.rights.license.spa.fl_str_mv	Atribución-NoComercial-CompartirIgual 4.0 Internacional
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dc.format.extent.spa.fl_str_mv	xi, 53 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.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González Osorio, Fabio Augusto35912f60905ba6e179208c70e6024e80Cruz Roa, Ángel Alfonso46998d223286d3d4d34f7436c6934037Medina Carrillo, Sebastian Rodrigo7a657ca7a275e64773b435de71d59cf6Romero, EduardoTabares Soto, ReinelMindlab2024-07-17T02:18:49Z2024-07-17T02:18:49Z2024https://repositorio.unal.edu.co/handle/unal/86504Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Gleason grading is recognized as the standard method for diagnosing prostate cancer. However, it is subject to significant inter-observer variability due to its reliance on subjective visual assessment. Current deep learning approaches for grading often require exhaustive pixel-level annotations and are generally limited to patch-level predictions, which do not incorporate slide-level information. Recently, weakly-supervised techniques have shown promise in generating whole-slide label predictions using pathology report labels, which are more readily available. However, these methods frequently lack visual and quantitative interpretability, reinforcing the black box nature of deep learning models, hindering their clinical adoption. This thesis introduces WiSDoM, a novel weakly-supervised and interpretable approach leveraging attention mechanisms and Kernel Density Matrices for the grading of prostate cancer on whole slides. This method is adaptable to varying levels of supervision. WiSDoM facilitates multi-scale interpretability through several features: detailed heatmaps that provide granular visual insights by highlighting critical morphological features without requiring tissue annotations; example-based phenotypical prototypes that illustrate the internal representation learned by the model, aiding in clinical verification; and visual-quantitative measures of model uncertainty, which enhance the transparency of the model's decision-making process, a crucial factor for clinical use. WiSDoM has been validated on core-needle biopsies from two different institutions, demonstrating robust agreement with the reference standard (quadratically weighted Kappa of 0.93). WiSDoM achieves state-of-the-art inter-observer agreement performance on the PANDA Challenge publicly available dataset while being clinically interpretable.La clasificación de Gleason se reconoce como el método estándar para diagnosticar el cáncer de próstata. Sin embargo, está sujeto a una variabilidad significativa entre observadores debido a su dependencia de la evaluación visual subjetiva. Los enfoques actuales de aprendizaje profundo a menudo requieren anotaciones exhaustivas a nivel de píxeles y generalmente se limitan a predicciones a nivel de parche, que no incorporan información a nivel de lámina. Recientemente, las técnicas débilmente supervisadas se han mostrado prometedoras a la hora de generar predicciones de etiquetas de láminas completas utilizando etiquetas de informes de patología, que están más fácilmente disponibles. Sin embargo, estos métodos frecuentemente carecen de interpretabilidad visual y cuantitativa, lo que refuerza la naturaleza de caja negra de los modelos de aprendizaje profundo y dificulta su adopción clínica. Esta tesis introduce WiSDoM, un enfoque novedoso interpretable y débilmente supervisado que aprovecha los mecanismos de atención y las matrices de densidad para gradar cáncer de próstata en láminas completas. Este método se adapta a distintos niveles de supervisión. WiSDoM facilita la interpretabilidad a múltiples escalas a través de varias características: mapas de calor detallados que brindan información visual granular al resaltar características morfológicas críticas sin requerir anotaciones de tejido; prototipos fenotípicos basados en ejemplos que ilustran la representación interna aprendida por el modelo, ayudando en la verificación clínica; y medidas visual-cuantitativas de incertidumbre del modelo, que mejoran la transparencia del proceso de toma de decisiones, un factor crucial para el uso clínico. WiSDoM se ha validado en biopsias de dos instituciones diferentes, lo que demuestra una sólida concordancia con el estándar de referencia (Kappa ponderado cuadráticamente de 0,93). WiSDoM logra un rendimiento del estado del arte de acuerdo entre observadores en el conjunto de datos PANDA Challenge además de ser clínicamente interpretable. (Texto tomado de la fuente).Research reported in this publication was partially supported by projects BPIN 2019000100- 060 ”Implementation of a Network for Research, Technological Development and Innovation in Digital Pathology (RedPat) supported by Industry 4.0 technologies” from FCTeI of SGR resources, approved by OCAD of FCTeI and MinCiencias, and project 110192092354, en- titled ”Program for the Early Detection of Premalignant Lesions and Gastric Cancer in urban, rural and dispersed areas in the Department of Nariño” of call No. 920 of 2022 of MinCiencias.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónSistemas Inteligentesxi, 53 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud::616 - Enfermedades000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresAprendizaje ProfundoNeoplasias de la Próstata/diagnóstico por imagenPatologíaDeep LearningProstatic Neoplasms/diagnostic imagingPathologyProstate cancerHistopathologyDeep LearningCancer gradingDensity matrixInterpretabilityCáncer de prostataHistopatologíaAprendizaje automáticoGradación de cáncerMatriz de densidadInterpretabilidadA Deep Learning model for automatic grading of prostate cancer histopathology imagesModelo de Deep Learning para la gradación automática de imágenes histopatológicas de cáncer de próstataTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMBulten et al. 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Histoprep: Preprocessing large medical images for machine learning made easy! https://github.com/jopo666/HistoPrep, 2022.InvestigadoresPúblico generalORIGINAL1020805304.2024.pdf1020805304.2024.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemas y Computaciónapplication/pdf57892625https://repositorio.unal.edu.co/bitstream/unal/86504/4/1020805304.2024.pdf98d00746c3d569943bcecc1ee169e8daMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86504/5/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD55THUMBNAIL1020805304.2024.pdf.jpg1020805304.2024.pdf.jpgGenerated Thumbnailimage/jpeg4383https://repositorio.unal.edu.co/bitstream/unal/86504/6/1020805304.2024.pdf.jpged7dd65ad137beb27a142d2cb2475b52MD56unal/86504oai:repositorio.unal.edu.co:unal/865042024-07-16 23:04:53.681Repositorio Institucional Universidad Nacional de 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A Deep Learning model for automatic grading of prostate cancer histopathology images

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