Technology played a crucial role in combating the COVID pandemic, both in the rapid development of vaccines and the early detection of the virus. Consequently, numerous studies in the medical field have focused on leveraging the power of artificial intelligence for COVID-19 detection. However, in the medical domain, it is essential to have a clear understanding of the processes and algorithms used in decision-making, as these directly impact people’s health. Therefore, efforts have been made to implement explainable artificial intelligence techniques, enabling humans to understand and explain the deep learning algorithms used in disease detection. In this work, we present a novel approach to detecting COVID-19 in chest X-rays using a Variational Autoencoder model to identify lung anomalies. This methodology aims to highlight critical areas of the image, allowing healthcare professionals to identify them more effectively. Additionally, it seeks to provide clearer explanations of the decisions made by the artificial i ntelligence, r educing the complexity of the "black boxes" generated by deep learning neural networks. With this methodology, we hope to improve the effectiveness and reliability of early COVID-19 detection through chest X-rays.

Recognizing variable stars is a task of interest in the astronomy community. Currently, this task has taken advantage of deep learning algorithms. However, these algorithms require a large amount of data to achieve high levels of precision. In this work, self-supervised learning is proposed to improve the classification of variable stars considering a reduced amount of data using recurrent networks. The experiments in Gaia dataset show that the proposed approach allows to improve performance, when compared with traditional initialization schemes, up to 7% and 13% in real databases in semi-supervised learning scenarios. In future work, we propose considering experiments with other variable star databases.