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  • Colloquium on Digital Transformation Science
  • Translating Prior AI Research in Breast Cancer Imaging to Interrogate Thoracic Images of COVID-19

    You are cordially invited to the launch of the Colloquium on Digital Transformation Science, Thursday, July 9, 1 pm PT/4 pm ET. Maryellen Giger, The University of Chicago's A.N. Pritzker Professor of Radiology, will give a lecture followed by a Q&A on her team's groundbreaking computational techniques for investigating medical images. 

    Register here. 

    The COVID-19 pandemic presents a pressing public health need for computational techniques to augment the interpretation of medical images in their role for: surveillance and early detection of COVID-19 resurgence via monitoring of medical imaging data; detection, triaging, and differential diagnosis of COVID-19 patients; and prognosis, including prediction and monitoring of response, for use in patient management. While thoracic imaging, including chest radiography and computed tomography, are being re-examined for their role in patient management, the limitations for improved interpretation are partially due to the qualitative interpretation of the images. Professor Giger and her colleagues at University of Chicago and Argonne National Laboratory aim to develop machine intelligence methods to aid in the interrogation of medical images from COVID-19 patients. They draw on decades of AI development of medical images to quantify and explain the COVID-19 presentation on imaging, specifically through machine learning methods of interrogating cancer on multi-modality breast images for “virtual biopsies.” 

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