Invited Talk – Mert Sabuncu (Cornell U): Deep Learning for Compressed Imaging, Oct 12th 2021, 16h CET

Symbolic picture for the article. The link opens the image in a large view.

It’s a great pleasure to announce Meet Sabuncu as invited speaker at out lab!

Title: Deep Learning for Compressed Imaging
Date: Oct 12th 2021, 16h CET

Abstract: Imaging techniques such as MRI can be accelerated by sampling below the Shannon-Nyquist rate via compressed sensing. In this talk, I will consider the use of deep learning methods for this problem. First, I will present our approach for Learning-based Optimization of the Under-sampling PattErn, or LOUPE. For a given sparsity constraint, LOUPE optimizes the under-sampling pattern and reconstruction model simultaneously, using a computationally efficient end-to-end deep learning strategy. Our experiments with MRI and microscopy demonstrate that LOUPE-derived patterns yield significantly more accurate reconstructions compared to standard under-sampling schemes. I will then switch gears and focus on the reconstruction problem only and presents some deep-learning based innovations that we have recently proposed, including the use of hyper-networks that give end-users to ability to choose from multiple reconstructions that are consistent with data.

Short Bio: Mert R. Sabuncu received a PhD degree in Electrical Engineering from Princeton University, where his dissertation dealt with biomedical image registration. Mert then moved to the Massachusetts Institute of Technology for a post-doc at the Computer Science and Artificial Intelligence Lab, where he worked on a range of biomedical image analysis problems, including the segmentation of brain MRI scans. After his post-doc at MIT, Mert was a faculty member at the A.A Martinos Center for Biomedical Imaging (Massachusetts General Hospital and Harvard Medical School), where he built a research program on algorithmic tools for integrating large-scale genetics and medical imaging datasets. Today, Mert is Associate Professor in the School of Electrical and Computer Engineering at Cornell University and Cornell Tech, in New York City. His research group develops machine learning based computational tools for biomedical imaging applications. He is a recipient of an NSF CAREER Award (2018) and an NIH Early Career Development Award (2011).

Check out more invited talks here: