Interval Neural Networks as Instability Detectors for Image Reconstructions
by Jan Macdonald, Maximilian März, Luis Oala, Wojciech Samek
Year:
2021
Publication:
Springer Vieweg
Abstract:
This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks. Although neural networks often empirically outperform traditional reconstruction methods, their usage for sensitive medical applications remains controversial.
Link:
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Brief introduction of the dida co-author(s) and relevance for dida's ML developments.
About the Co-Author
During his studies in mathematics (TU Berlin) Jan focussed on applied topics in optimization, functional analysis, and image processing. His doctoral studies (TU Berlin) explored the interplay between theoretical and empirical research on neural networks. This resulted in his PhD thesis investigating the reliability of deep learning for imaging and computer vision tasks in terms of interpretability, robustness, and accuracy. At dida he works as a Machine Learning Researcher at the interface of scientific research and software development.