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Deep learning

Working Group 8.42

Overview

Deep learning belongs to the class of machine learning methods and typically employs neural networks with many layers to solve tasks such as classification or function approximation. Due to their flexibility neural networks are widely applicable and have shown extraordinary performance, for example in autonomous driving, computer-aided diagnosis, or the automatic segmentation of images. To make deep learning applicable for metrology, it is crucial to understand and evaluate the reliability of these methods. One important issue in this regard is to quantify the uncertainties associated with their results. Models employed in metrology are usually well understood and often based on physical knowledge. Deep learning, on the other hand, builds its models directly from data. Another challenge in the application of deep learning for metrology therefore is to understand the behavior of the empirically built models.

Example for regression by a neural network and quantification of the associated uncertainty.

Research

Fundamental aspects:

  • Uncertainty quantification
  • Bayesian inversion using daten-driven priors
  • Explainability
  • Adversarial Machine learning

Applications:

  • Image quality assessment in mammography
  • Qualitative and quantitative MR imaging
  • Inverse problems in optics

PhD theses

Software

Publications

N. Amanova, J. Martin;C. Elster
Applied Intelligence,
2024.
J. Faller, N. Amanova, R. E. van Engen, J. Martin;C. Elster
Machine Learning: Science and Technology,
2023.
M. Marschall, G. Wübbeler, F. Schmähling;C. Elster
Metrologia, 60(4),
2023.
S. Brahma, C. Kolbitsch, J. Martin, T. Schäffter;A. Kofler
Medical Physics,
2023.
M. Marschall, G. Wübbeler, F. Schmähling;C. Elster
Computational Statistics,
2023.
J. Martin;C. Elster
Neural Processing Letters,
2022.
M. Olbrich, L. Riazy, T. Kretz, T. Leonard, D. van Putten, M. Bär, K. Oberleithner;S. Schmelter
International Journal of Multiphase Flow,
2022.
L. Harren née Hoffmann
PhD Thesis
2022.
F. Schmähling, J. Martin;C. Elster
Applied Intelligence,
2022.
N. Amanova, J. Martin;C. Elster
Machine Learning: Science and Technology,
2022.
T. Mehari;N. Strodthoff
Computers in Biology and Medicine, 141
105114,
2021.
L. Hoffmann, I. Fortmeier;C. Elster
tm - Technisches Messen,
2021.
L. Hoffmann;C. Elster
2021.
L. Hoffmann, I. Fortmeier;C. Elster
Machine Learning: Science and Technology,
2021.
J. Martin
2021.
J. Martin;C. Elster
Appl Intell,
2020.
T. Kretz
PhD Thesis
2020.
L. Hoffmann;C. Elster
Journal of Sensors and Sensor Systems, 9
301--307,
2020.
T. Kretz, K.-R. Müller, T. Schäffter;C. Elster
IEEE Transactions on Biomedical Engineering,
2020.
J. Martin;C. Elster
Neurocomputing, 382
80--86,
2020.
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Preprints