Reliable AI in Medical Imaging: Successes, Challenges, and Limitations
Online Talk
Zoom Registration
https://dkfz-de.zoom-x.de/meeting/register/u50lcuCrpz8uH9dIOM8SF5yL3nJHx88nLCWE
Abstract
Deep neural networks as the current work horse of artificial intelligence have already been tremendously successful in real-world applications, ranging from science to public life. The area of (medical) imaging sciences has been particularly impacted by deep learning-based approaches, which sometimes by far outperform classical approaches for particular problem classes. However, one current major drawback is the lack of reliability of such methodologies.
In this lecture we will first provide an introduction into this vibrant research area. We will then present some recent advances, in particular, concerning optimal combinations of traditional model-based methods with AI-based approaches in the sense of true hybrid algorithms, with a particular focus on limited-angle computed tomography. Due to the importance of explainability for reliability, we will also touch upon this area by highlighting an approach which is itself reliable due to its mathematical foundation. We will finish with a look into the future of (green) AI computing.
Biosketch
Gitta Kutyniok (https://www.ai.math.lmu.de/kutyniok) currently has a Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at the Ludwig-Maximilians-Universität München, and is in addition affiliated with the DLR-German Aerospace Center and the University of Tromso. She received her Diploma in Mathematics and Computer Science as well as her Ph.D. degree from the Universität Paderborn in Germany, and her Habilitation in Mathematics in 2006 at the Justus-Liebig Universität Gießen. From 2001 to 2008 she held visiting positions at several US institutions, including Princeton University, Stanford University, Yale University, Georgia Institute of Technology, and Washington University in St. Louis. In 2008, she became a full professor of mathematics at the Universität Osnabrück, and moved to Berlin three years later, where she held an Einstein Chair in the Institute of Mathematics at the Technische Universität Berlin and a courtesyappointment in the Department of Computer Science and Engineering until 2020. In 2023, together with colleagues she founded the start-up EcoLogic Computing GmbH.
Gitta Kutyniok has received various awards for her research such as an award from the Universität Paderborn in 2003, the Research Prize of the Justus-Liebig Universität Gießen and a Heisenberg-Fellowship in 2006, and the von Kaven Prize by the DFG in 2007. She was invited as the Noether Lecturer at the ÖMG-DMV Congress in 2013, a plenary lecturer at the 8th European Congress of Mathematics (8ECM) in 2021, and the lecturer of the London Mathematical Society (LMS) Invited Lecture Series in 2022. She was also honored by invited lectures at both the International Congress of Mathematicians 2022 (ICM 2022) and the International Congress on Industrial and Applied Mathematics (ICIAM 2023). Moreover, she was elected as a member of the Berlin-Brandenburg Academy of Sciences and Humanities in 2017 and of the European Academy of Sciences in 2022, became a SIAM Fellow in 2019 and an IEEE Fellow in 2024, and served as Vice President-at-Large of SIAM from 2021 to 2023. She currently acts as LMU-Director of the Konrad Zuse School of Excellence in Reliable AI (relAI) in Munich and is spokesperson of the DFG-Priority Program "Theoretical Foundations of Deep Learning" and of the AI-HUB@LMU, which is the interdisciplinary platform for research, teaching, and
transfer in AI and data science at LMU.
Gitta Kutyniok's research work covers, in particular, the areas of applied and computational harmonic analysis, artificial intelligence, compressed sensing, deep learning, imaging sciences, inverse problems, and applications to life sciences, robotics, and telecommunication.