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Probabilistic Inference in Medical Imaging via Conditional GANs | Assad Oberai, PhD


For those of you who were unable to join the webinar live, the stream is now available for everyone to watch here or on YouTube.


Many medical imaging tasks, like image de-noising, de-blurring, imputation, synthesis, and inference, require transformation of field data of one type to another. This transformation is often challenging to perform because it is not well defined mathematically, it is tedious perform manually, or it yields multiple likely solutions.

In this talk we present a probabilistic deep learning algorithm based on adversarial learning to solve this class of problems. We describe how, given samples from the joint distribution of two types of images (input and output), this algorithm learns the distribution of the output image conditioned on the input image and samples efficiently from this distribution. Thereafter we present applications of this algorithm in a variety of tasks including, brain extraction in MR images, image imputation in Contrast Enhanced CT of renal tumors and inferring images of mechanical properties from displacement fields acquired using ultrasound.

Speaker Bio:

Assad Oberai is the Hughes Professor of Aerospace and Mechanical Engineering in the Viterbi school of engineering at the University of Southern California (USC). He earned a Bachelor of Engineering degree from Osmania University (India) in 1992, an MS from the University of Colorado in 1994, and a PhD from Stanford University in 1998, all in Mechanical Engineering. He was an Assistant Professor at Boston University (2001-2005), and Rensselaer Polytechnic Institute (RPI) in 2006. At RPI, he was the Associate Dean for Research and Graduate Studies in the School of Engineering, and the Associate Director of the Scientific Computation Research Center. He joined USC in January
2018 and was the Vice Dean for Research in the School of Engineering from 2019-2021.

Assad leads the Computation and Data Driven Discovery (CD3) group which designs, implements and applies data- and physics-based models and algorithms to solve problems in engineering and science. Problems such as better detection, diagnosis and care of diseases like cancer, understanding the role of mechanics and physics in medicine and biology, modeling the evolution of multi-physics and multiscale systems, and reduced-order models for aerospace and mechanical systems. He has authored more than 100 articles in archival journals on these topics. He is on the board of academic editors for three journals.

Assad is a Fellow of the American Society of Mechanical Engineers (ASME, 2020), the American Institute of Medical and Biological Engineering (AIMBE, 2016), and the United States Association of Computational Mechanics (USACM, 2015). In 2015, he was awarded the Research Excellence Award by the School of Engineering at Rensselaer.
He received the Humboldt Foundation Award for experienced researchers in 2009, and the Erasmus Mundus Master Course Lectureship at Universidad Politécnica de Cataluña, Barcelona in 2010. He was awarded the Thomas J.R. Hughes Young Investigator Award for his contributions to Applied Mechanics by the ASME in 2007. He is a recipient of the National Science Foundation Career award in 2005 and the Department of Energy Early Career award in 2004.

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