GIDRM 2020 Keynote - Known Operator Learning - An approach to unite Machine Learning and Physics
Channel: Andreas Maier
Date: 2021-02-25

Keynote on GIRDM 2020
https://gidrm2020.uniroma2.it

Abstract
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from computed tomography image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such, the concept is widely applicable for many researchers in physics, imaging and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging and signal processing.

References
[1] Florin Ghesu et al. Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data. Medical Image Computing and Computer-Assisted Intervention MICCAI 2017 (MICCAI), Quebec, Canada, pp. 194-202, 2017 – MICCAI Young Researcher Award
[2] Florin Ghesu et al. Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans. IEEE Transactions on Pattern Analysis and Machine Intelligence. ePub ahead of print. 2018
[3] Bastian Bier et al. X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery. MICCAI 2018 – MICCAI Young Researcher Award
[4] Yixing Huang et al. Some Investigations on Robustness of Deep Learning in Limited Angle Tomography. MICCAI 2018.
[5] Andreas Maier et al. Precision Learning: Towards use of known operators in neural networks. ICPR 2018. 
[6] Tobias Würfl, Florin Ghesu, Vincent Christlein, Andreas Maier. Deep Learning Computed Tomography. MICCAI 2016. 
[7] Hammernik, Kerstin, et al. "A deep learning architecture for limited-angle computed tomography reconstruction." Bildverarbeitung für die Medizin 2017. Springer Vieweg, Berlin, Heidelberg, 2017. 92-97.
[8] Aubreville, Marc, et al. "Deep Denoising for Hearing Aid Applications." 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE, 2018.
[9] Christopher Syben, Bernhard Stimpel, Jonathan Lommen, Tobias Würfl, Arnd Dörfler, Andreas Maier. Deriving Neural Network Architectures using Precision Learning: Parallel-to-fan beam Conversion. GCPR 2018. https://arxiv.org/abs/1807.03057 
[10] Stromer, Daniel, et al. "Browsing through sealed historical manuscripts by using 3-D computed tomography with low-brilliance X-ray sources." Scientific reports 8.1 (2018): 15335.
[11] Stromer, Daniel, et al. "Virtual cleaning and unwrapping of non-invasively digitized soiled bamboo scrolls." Scientific reports 9.1 (2019): 2311.

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Music Reference: 
Damiano Baldoni - Thinking of You (Intro)
https://freemusicarchive.org/music/Damiano_Baldoni/Old_Beat/Thinking_of_you_1513
Damiano Baldoni - Poenia (Outro)
https://freemusicarchive.org/music/Damiano_Baldoni/Crystal_Lake/Poenia