Title: Unpacking Data Needs for Deep Learning Based Imaging
Abstract: Deep neural networks trained on example images enable a new generation of consumer, medical, and scientific imaging systems. I will start this talk with highlighting the role of training data. Surprisingly little data is needed for obtaining state-of-the-art performance. Moreover, it is possible to learn effectively without clean ground-truth data with self-supervised learning methods. However, the excellent performance of deep networks for imaging can be deceiving, as it is usually measured by training and testing on similar looking data, say on data from one hospital. By evaluating neural networks in a variety of practical scenarios, for example by training and evaluating networks on data from different hospitals, we identified a key challenge in deep learning based imaging: robustness to distribution shifts. Finally, I will discuss model and data driven approaches for building robust deep learning based imaging methods.
Bio: Reinhard Heckel is a Rudolf Moessbauer assistant professor in the Department of Computer Engineering at the Technical University of Munich, and an adjunct assistant professor at Rice University, where he was an assistant professor in the ECE department from 2017-2019. Before that, he was a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and a researcher at the Cognitive Computing & Computational Sciences Department at IBM Research Zurich. He completed his PhD in electrical engineering in 2014 at ETH Zurich and was a visiting PhD student at the Statistics Department at Stanford University. Reinhard is working in the intersection of machine learning and signal/information processing with a current focus on deep networks for solving inverse problems, developing foundations and methods for machine learning, and DNA data storage.