Holds a bachelor's degree in Molecular Sciences from the University of São Paulo (2020) and a master's degree in Computer Science from the Institute of Mathematics and Statistics at USP (IME-USP). Works in the field of artificial intelligence and computer vision, focusing on the use of machine learning for medical image analysis. During the master's program, developed the dissertation "Classification of Chest CT Images in the Context of COVID-19", exploring computational methods to assess the extent of pulmonary involvement in COVID-19 cases using computed tomography scans.Has experience in deep learning, medical image classification, and the development of prediction aggregation models. During the undergraduate studies, conducted research internships in experimental neuroscience (UFABC), studying the function of the prefrontal cortex in rats performing temporal perception tasks, and in applied artificial intelligence (IME-USP), comparing machine learning techniques for customer transaction prediction and analysis of tweets about natural disasters.Currently, is interested in continuing research at the intersection of computer vision and medical applications, exploring machine learning approaches for the analysis and interpretation of medical imaging. (Source: Lattes Curriculum)
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Lung computed tomography (CT) images are being used to help diagnosis and monitoring of COVID-19 disease. In order to facilitate this task, automated methods for detecting COVID-19 lesions are being developed. These methods are mostly based on deep learning techniques and are already reaching high classification accuracy. However, most of the existing works are restricted to classifying i…
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