Identificación automática de marcadores patológicos en imágenes de histopatología

Abstract. The inter and intra subject variability is a common problem in several tasks associated to the examination of histopathological samples. This variability might hinder the evaluation of cancerous diseases. The development of automatic image analysis techniques and computerized aided diagnostic tools in pathology aims to reduce the impact of such variability by offering quantitative measurements and estimations. These measurements allow an accurate evaluation and classification of the diseases in virtual slide images. The main problem addressed in this thesis is evaluating the correlation of the automated identification of pathological markers with cancer malignancy and aggresivenes. Hence, a set of classifier models are trained to detect known pathological patterns. The classifiers are then used to quantify the presence of the pathological markers. Finally, the resulting measurements are correlated with the cancer risk recurrence. Results show that the automated detectors are able to quantify patterns that show differences across several cancer risk groups.

• Autores: Romo Bucheli, David Edmundo
• Tipo de recurso: Doctoral thesis
• Fecha de publicación: 2017
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: spa
• OAI Identifier: oai:repositorio.unal.edu.co:unal/59250
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/59250; http://bdigital.unal.edu.co/56604/
• Palabra clave: 61 Ciencias médicas; Medicina / Medicine and health; 62 Ingeniería y operaciones afines / Engineering; Histopathology; Digital pathology; Pathological marker; Histopatología; Patología digital; Marcador patológico
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional