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COVID+DS: Analysis of chest CT imaging data and connection to COVID diagnosis

Jul 28

Tuesday, July 28, 2020 - 4:00pm to 5:00pm

Virtual session

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Presenter

Rachel Draelos

Medical image analysis with machine learning holds immense promise for accelerating the radiology workflow and benefiting patient care. Chest computed tomography (CT) is a medical imaging technique that produces a high-resolution volumetric image of the heart and lungs. Chest CT can be used to diagnose a wide variety of conditions including cancer, fractures, and infections like COVID-19. However, interpreting a chest CT scan requires over 12 years of postsecondary education and painstaking manual inspection of hundreds of 2D slices. There is thus significant interest in developing machine learning models that can automatically interpret chest CT images. In this session, a variety of machine learning models for automated chest CT interpretation are introduced, including slice and volume-based convolutional neural networks. Furthermore, recent literature proposing automatic COVID-19 diagnosis from chest CT scans is reviewed and analyzed. This session is part of the Duke+Data Science (+DS) program virtual series on COVID-19 + Data Science. Please join us for a 8-week series on data science methods with direct applications to the COVID-19 pandemic. Learn from Duke experts about the state-of-the-art in these 1-hour virtual sessions. For more information, please visit https://plus.datascience.duke.edu