ECE Seminar by Boris Gutman: Can We Predict Brain Disease by Analyzing Brain Scans?
Armour College of Engineering’s Department of Electrical and Computer Engineering will welcome Boris Gutman, Assistant Professor of Biomedical Engineering at Illinois Institute of Technology, to present a lecture, “Can We Predict Brain Disease by Analyzing Brain Scans?"
Abstract: Brain imaging offers a world of possibilities, potentially linking fine-grained 3D image data with cognitive function. As humans, we like to predict all manner of things, not least of them being our physical health. And while there is much excitement in the world of artificial intelligence about the ever-improving accuracy with which we can predict things, we are often less concerned with domain-specific relevance and utility of the prediction.
This is especially true for brain MRI and neurodegenerative disease. Simple binary questions such as, “Does this patient have disease X?” or even, “Will the patient acquire disease X in Y years?” have proven less interesting to basic researchers and health professionals than, “How quickly will the patient’s health deteriorate?”, “When and in what order will future symptoms appear?” and “What are the connections among the observable biomarkers and between biomarkers and symptom onset?”
Disease progression models (DPMs) attempt to answer the more interesting questions. In this talk, we will focus on applications of DPMs to brain imaging and neurodegenerative disease. We will start from an intuitive description of image analysis methods for brain MRI, and then go over some recent imaging-DPM developments.
Biography: Boris Gutman is an Assistant Professor of Biomedical Engineering at Illinois Tech and a member of Illinois Tech’s Medical Imaging Research Center. He received all his degrees from UCLA – B.S. in Applied Math (2006) and Ph.D. in Biomedical Engineering (2013). He conducted post doctoral research in neuroimaging genetics at University of Southern California from 2013-2017. In his research, Dr. Gutman develops computational methods to jointly analyze brain MR images arising from multiple modalities, as well as discover the genetic underpinning of healthy brain variation and brain-related disorders.