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

Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models based on Convolutional Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. Most of the current work address this problem as a binary classification task. However, including the grade estimation and quantification of predictions uncertainty can potentially increase the robustness of the model. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representational power of deep learning, with the ability to generalize from small datasets of Gaussian process models. The results show that uncertainty quantification in the predictions improves the interpretability of the method as a diagnostic support tool. The source code to replicate the experiments is publicly available at https://github.com/stoledoc/DLGP-DR-Diagnosis

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

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 diagnósticoDiabetic retinopathy (DR) is one of the microvascular complications of diabetes mellitus, which remains a leading cause of blindness worldwide. Computational models based on convolutional neural networks represent the state of the art for automatic detection of DR using fundus images. Most of the current work addresses this problem as a binary classification task. However, including law estimation and quantification of prediction uncertainty can potentially increase model robustness. In this paper, a hybrid deep learning and Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representational power of deep learning with the ability to generalize from small data sets of Gaussian process models. The results show that the quantification of uncertainty in the predictions improves the interpretability of the method as a diagnostic support tool. Translated with www.DeepL.com/Translator (free version)Este trabajo fue parcialmente financiado por un premio de investigación de Google y por el proyecto Colciencias número 1101-807-63563

Methods for dynamic investigations of surface-attached in vitro bacterial and fungal biofilms.

Three dynamic models for the investigation of in vitro biofilm formation are described in this chapter. In the 6-well plate assay presented here, the placing of the plate on a rotating platform provides shear, thereby making the system dynamic with respect to the static microtiter assay. The second reported model, especially suitable for harvesting high amounts of cells for transcriptomic or proteomic investigations, is based on numerous glass beads placed in a flask incubated with shaking on a rotating platform, thus increasing the surface area for biofilm formation. Finally, the flow-cell system, that is the driving model for elucidating the biofilm-forming process in vitro as well as the biofilm tolerance towards antibiotics and host defense components, is illustrated here