Version 1
: Received: 18 September 2023 / Approved: 19 September 2023 / Online: 20 September 2023 (08:42:06 CEST)
How to cite:
Nemirovsy-Rotman, S.; Bercovich, E. Physics-Driven Deep Learning for Computer Aided Diagnostics Tasks in Medical Imaging. Preprints2023, 2023091312. https://doi.org/10.20944/preprints202309.1312.v1
Nemirovsy-Rotman, S.; Bercovich, E. Physics-Driven Deep Learning for Computer Aided Diagnostics Tasks in Medical Imaging. Preprints 2023, 2023091312. https://doi.org/10.20944/preprints202309.1312.v1
Nemirovsy-Rotman, S.; Bercovich, E. Physics-Driven Deep Learning for Computer Aided Diagnostics Tasks in Medical Imaging. Preprints2023, 2023091312. https://doi.org/10.20944/preprints202309.1312.v1
APA Style
Nemirovsy-Rotman, S., & Bercovich, E. (2023). Physics-Driven Deep Learning for Computer Aided Diagnostics Tasks in Medical Imaging. Preprints. https://doi.org/10.20944/preprints202309.1312.v1
Chicago/Turabian Style
Nemirovsy-Rotman, S. and Eyal Bercovich. 2023 "Physics-Driven Deep Learning for Computer Aided Diagnostics Tasks in Medical Imaging" Preprints. https://doi.org/10.20944/preprints202309.1312.v1
Abstract
Deep Neutral Networks (DNNs) were initially proposed towards the midst of the 20th century, motivated by the neural structure and mechanism of the human brain. Thanks to major ad-vancements in computational resources, during the past decade DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and gen-eralizable networks is a major challenge, especially in critical applications, such as medical Computer Aided Diagnostics (CAD) and other medical imaging tasks, including classification, regression and reconstruction. Within this context, a line of physics-driven approaches for deep learning has recently emerged, aimed at improving the stability and generalization capacity of DNNs for medical imaging applications. In this paper, we review recent work focused on phys-ics-driven or prior-information learning for a variety of imaging modalities and medical applica-tions. We discuss how the inclusion of domain-related knowledge into the learning process and networks’ design supports their stability and generalization capability. In addition, we highlight current and future challenges within this scope.
Keywords
deep learning; deep neural networks; robustness; stability; generalization; physics-driven learning; medical imaging; computer aided diagnostics
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
