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Abstract #1793

Analysing The Role Of Model Uncertainty in Flourine-19 MRI using Markov Chain Monte Carlo methods

Masoumeh Javanbakhat1, Ludger Starke2, Sonia Waiczies2, and Christoph Lippert1
1Digital Health-Machine Learning Group, Hasso Plattner Institute, Potsdam, Germany, 2Berlin Ultrahigh Field Facility, Max Delbrück Centre for Moleculare Medicine in the Helmholtz Association, Berlin, Germany


Deep learning (DL) has achieved state of the art results in semantic segmentation of numerous medical imaging applications. Despite promising results deep learning models tend to produce point estimates as outputs which leads to overconfident, miscalibrated predictions. These overconfident predictions are specifically problematic in medical applications. Hence, providing a measure of a system’s confidence to identify untrustworthy predictions is essential to guide clinical decisions. Here we propose a 3D Bayesian segmentation model to provide uncertainty estimation for the Fluorine-19 MRI dataset based on Stochastic Gradient Markov Chain Monte Carlo methods.

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