Success at MICCAI Educational Challenge at the Pattern Recognition Lab
It is a pleasure to announce that the Pattern Recognition Lab was again successful at the MICCAI Educational Challenge. This year a submission by Andreas Maier entitled “How to “Hack” Proprietary AI Image Processing Software” won the third prize:
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.
Blog Post: https://towardsdatascience.com/how-we…
PYCONRAD: https://git5.cs.fau.de/PyConrad/pyCONRAD (try pip install pyconrad)