Polina Golland is the Sunlin (1966) Priscilla Chou professor of Electrical Engineering and Computer Science and a principal investigator in the Computer Science and Artifical Intelligence Labotory (CSAIL) at MIT. Polina's primary research interest is in developing novel techniques for biomedical image analysis and understanding. She particularly enjoys working on algorithms that either explore the geometry of the world and the imaging process in a new way or improve image-based inference through statistical modeling of the image data. Polina is interested in shape modeling and representation, predictive modeling and visualization of statistical models. Her current research focuses on developing statistical analysis methods for characterization of biological processes based on image information. In this domain, she is interested in modeling biological shape and function, how they relate to each other and vary across individuals.
The seminar will take place in-person in Meeting Room 2, Pavilion A, Centre for Mathematical Sciences, Cambridge.
Title: Learning to read x-ray: applications to heart failure monitoring
Abstract: We propose and demonstrate a novel approach to training image classification models based on large collections of images with limited labels. We take advantage of availability of radiology reports to construct joint multimodal embedding that serves as a basis for classification. We demonstrate the advantages of this approach in application to assessment of pulmonary edema severity in congestive heart failure that motivated the development of the method.
