Deep Learning - Known Operator Learning Part 4

This is the final video of the class in which we present more applications of known operator learning. Furthermore, the video also contains my personal interpretation of where the field is heading and what kind of research will follow next.

Video References:
X-Ray Material Decomposition

Further Reading:
A gentle Introduction to Deep Learning

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] Fu, Weilin, et al. "Frangi-net." Bildverarbeitung für die Medizin 2018. Springer Vieweg, Berlin, Heidelberg, 2018. 341-346.
[11] Fu, Weilin, Lennart Husvogt, and Stefan Ploner James G. Maier. "Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse." arXiv preprint arXiv:1911.02080 (2019).