Medical images contain a wealth of information about a patient but require a human expert to look at each image to capture relevant clinical knowledge. Computational tools that extract clinically important information from images enable development of novel biomarkers of disease, support surgical planning, and enable disease prognosis. From monitoring fetal development, to predicting stroke outcomes, this talk will discuss current challenges in extracting clinically actionable information from images.
Polina Golland is a professor of EECS at MIT CSAIL. She received her PhD from MIT and her Bachelor and Masters degree from Technion, Israel. Polina's primary research interest is in developing novel techniques for medical image analysis and understanding. With her students, she has demonstrated novel approaches to image segmentation, shape analysis, functional image analysis and population studies. Polina has served as an associate editor of the IEEE Transactions on Medical Imaging and of the IEEE Transactions on Pattern Analysis and Machine Intelligence. She is a Fellow of the International Society for Medical Image Computing and Computer Assisted Interventions.