Maier A, Yang SH, Maleki F, Muthukrishnan N, Forghani R (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 345-350
Conference Proceedings Title: Informatik aktuell
Event location: Heidelberg, DEU
ISBN: 9783658369316
DOI: 10.1007/978-3-658-36932-3_70
In the domain of medical image processing, medical device manufacturers protect their intellectual property in many cases by shipping only compiled software, i.e. binary code which can be executed but is difficult to be understood by a potential attacker. In this paper, we investigate how well this procedure is able to protect image processing algorithms. In particular, we investigate whether the computation of mono-energetic images and iodine maps from dual energy CT data can be reverse-engineered by machine learning methods. Our results indicate that both can be approximated using only one single slice image as training data at a very high accuracy with structural similarity greater than 0.98 in all investigated cases.
APA:
Maier, A., Yang, S.H., Maleki, F., Muthukrishnan, N., & Forghani, R. (2022). Offer Proprietary Algorithms Still Protection of Intellectual Property in the Age of Machine Learning?: A Case Study Using Dual Energy CT Data. In Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 345-350). Heidelberg, DEU: Springer Science and Business Media Deutschland GmbH.
MLA:
Maier, Andreas, et al. "Offer Proprietary Algorithms Still Protection of Intellectual Property in the Age of Machine Learning?: A Case Study Using Dual Energy CT Data." Proceedings of the German Workshop on Medical Image Computing, 2022, Heidelberg, DEU Ed. Klaus Maier-Hein, Thomas M. Deserno, Heinz Handels, Andreas Maier, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2022. 345-350.
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