A free web service for fast COVID-19 classification of chest X-Ray images

The coronavirus outbreak became a major concern for society worldwide. Technological innovation and ingenuity are essential to fight COVID-19 pandemic and bring us one step closer to overcome it. Researchers over the world are working actively to find available alternatives in different fields, such as the Healthcare System, pharmaceutic, health prevention, among others. With the rise of artificial intelligence (AI) in the last 10 years, IA-based applications have become the prevalent solution in different areas because of its higher capability, being now adopted to help combat against COVID-19. This work provides a fast detection system of COVID-19 characteristics in X-Ray images based on deep learning (DL) techniques. This system is available as a free web deployed service for fast patient classification, alleviating the high demand for standards method for COVID-19 diagnosis. It is constituted of two deep learning models, one to differentiate between X-Ray and non-X-Ray images based on Mobile-Net architecture, and another one to identify chest X-Ray images with characteristics of COVID-19 based on the DenseNet architecture. For real-time inference, it is provided a pair of dedicated GPUs, which reduce the computational time. The whole system can filter out non-chest X-Ray images, and detect whether the X-Ray presents characteristics of COVID-19, highlighting the most sensitive regions.

Recent Developments Combining Ensemble Smoother and Deep Generative Networks for Facies History Matching

Ensemble smoothers are among the most successful and efficient techniques currently available for history matching. However, because these methods rely on Gaussian assumptions, their performance is severely degraded when the prior geology is described in terms of complex facies distributions. Inspired by the impressive results obtained by deep generative networks in areas such as image and video generation, we started an investigation focused on the use of autoencoders networks to construct a continuous parameterization for facies models. In our previous publication, we combined a convolutional variational autoencoder (VAE) with the ensemble smoother with multiple data assimilation (ES-MDA) for history matching production data in models generated with multiple-point geostatistics. Despite the good results reported in our previous publication, a major limitation of the designed parameterization is the fact that it does not allow applying distance-based localization during the ensemble smoother update, which limits its application in large-scale problems. The present work is a continuation of this research project focusing in two aspects: firstly, we benchmark seven different formulations, including VAE, generative adversarial network (GAN), Wasserstein GAN, variational auto-encoding GAN, principal component analysis (PCA) with cycle GAN, PCA with transfer style network and VAE with style loss. These formulations are tested in a synthetic history matching problem with channelized facies. Secondly, we propose two strategies to allow the use of distance-based localization with the deep learning parameterizations.