Spatio-Temporal Interpretation of the Covid-19 Risk Factors Using Explainable Ai

Links between environmental conditions (e.g., meteorological factors and air quality) and COVID-19 infection/mortality have been reported worldwide. However, the existing statistical frameworks are insufficient to investigate the factors that increase the risk for COVID-19 in urban areas. In this paper, we extend the concept of machine learning-based predictive modelling for COVID-19 spread, proposing an explainable AI approach in order to i) prioritize the risk factors, ii) define the interconnections between them and iii) detect positive or negative influence of the factors with respect to COVID-19 morbidity and mortality.

Bioinformatics systems for monitoring and mitigating epidemics: The STAMINA Paradigm

It is a common case for locally restricted epidemics to be expanded into global pandemics, due to globalization and numerous contacts through international business and tourism hubs. Nowadays, it is more and more likely to import pathogens and viruses from locations all around the world. This has never been more evident than the COVID-19 pandemic, which transformed within months from a local outbreak to a world-encompassing pandemic with severe losses of life and disruption to social status quo. The mitigation of the effects of such pandemics, can happen only through holistic systems that are able to support healthcare authorities in an end-to-end basis. In this the STAMINA platform design, and discuss the envisioned system initially from a holistic point of view. Our aim is to elaborate on (i) Standards that will be followed during the implementation and the deployment of the individual tools and the platform. (ii) The STAMINA platform from a high-level interconnections' mapping and discuss the interfacing between the user and the platform. © 2021 ACM.

Deep learning models for COVID-19 infected area segmentation in CT images

Recent studies indicated that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia infected area segmentation in CT images for the detection of COVID-19. We explore the efficacy of U-Nets and Fully Convolutional Neural Networks in this task using real-world CT data from COVID-19 patients. The results indicate that Fully Convolutional Neural Networks are capable of accurate segmentation despite the class imbalance on the dataset and the man-made annotation errors on the boundaries of symptom manifestation areas, and can be a promising method for further analysis of COVID-19 induced pneumonia symptoms in CT images. © 2021 ACM.

Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images

We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images. © 2020 ACM.