TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Herzog, Lisa A1 - Murina, Elvis A1 - Dürr, Oliver A1 - Wegener, Susanne A1 - Sick, Beate T1 - Integrating uncertainty in deep neural networks for MRI based stroke analysis JF - Medical Image Analysis N2 - At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine when physicians rely on the results for making critical treatment decisions. In this work, we provide an entire framework to diagnose ischemic stroke patients incorporating Bayesian uncertainty into the analysis procedure. We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images with corresponding uncertainty information about the reliability of the prediction. For patient-level diagnoses, different aggregation methods are proposed and evaluated, which combine the individual image-level predictions. Those methods take advantage of the uncertainty in the image predictions and report model uncertainty at the patient-level. In a cohort of 511 patients, our Bayesian CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart. The best patient aggregation method yielded 95.89% of accuracy. Integrating uncertainty information about image predictions in aggregation models resulted in higher uncertainty measures to false patient classifications, which enabled to filter critical patient diagnoses that are supposed to be closer examined by a medical doctor. We therefore recommend using Bayesian approaches not only for improved image-level prediction and uncertainty estimation but also for the detection of uncertain aggregations at the patient-level. KW - Bayesian convolutional neural networks KW - Uncertainty KW - Magnetic resonance imaging KW - Ischemic stroke Y1 - 2020 SN - 1361-8423 SS - 1361-8423 SN - 1361-8415 SS - 1361-8415 U6 - https://doi.org/10.1016/j.media.2020.101790 DO - https://doi.org/10.1016/j.media.2020.101790 IS - Vol. 65, Art. 101790 SP - 21 S1 - 21 ER -