  We evaluate two different methods for the integration of prediction
uncertainty into diagnostic image classifiers to increase patient safety in
deep learning. In the first method, Monte Carlo sampling is applied with
dropout at test time to get a posterior distribution of the class labels
(Bayesian ResNet). The second method extends ResNet to a probabilistic approach
by predicting the parameters of the posterior distribution and sampling the
final result from it (Variational ResNet).The variance of the posterior is used
as metric for uncertainty.Both methods are trained on a data set of optical
coherence tomography scans showing four different retinal conditions. Our
results shown that cases in which the classifier predicts incorrectly correlate
with a higher uncertainty. Mean uncertainty of incorrectly diagnosed cases was
between 4.6 and 8.1 times higher than mean uncertainty of correctly diagnosed
cases. Modeling of the prediction uncertainty in computer-aided diagnosis with
deep learning yields more reliable results and is anticipated to increase
patient safety.
