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Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

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Gonzalez Osorio, Fabio
Perdomo Charry, Oscar Julian
Toledo Cortes, Santiago
De La Pava, Melissa
Abstract (English)
La retinopatía diabética (RD) es una de las complicaciones microvasculares de la diabetes mellitus, que sigue siendo una de las principales causas de ceguera en todo el mundo. Los modelos computacionales basados ​​en redes neuronales convolucionales representan el estado del arte para la detección automática de RD utilizando imágenes de fondo de ojo. La mayor parte del trabajo actual aborda este problema como una tarea de clasificación binaria. Sin embargo, incluir la estimación de leyes y la cuantificación de la incertidumbre de las predicciones puede aumentar potencialmente la solidez del modelo. En este artículo, se presenta un método de proceso híbrido de aprendizaje profundo y gaussiano para el diagnóstico de RD y la cuantificación de la incertidumbre. Este método combina el poder de representación del aprendizaje profundo con la capacidad de generalizar a partir de pequeños conjuntos de datos de modelos de procesos gaussianos. Los resultados muestran que la cuantificación de la incertidumbre en las predicciones mejora la interpretabilidad del método como herramienta de apoyo al diagnóstico
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10 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/1425
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