  At present, the majority of the proposed Deep Learning (DL) methods provide
point predictions without quantifying the models 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 single image-level predictions. Those methods take
advantage of the uncertainty in 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.
