  Deep learning (DL) methods have in recent years yielded impressive results in
medical imaging, with the potential to function as clinical aid to
radiologists. However, DL models in medical imaging are often trained on public
research cohorts with images acquired with a single scanner or with strict
protocol harmonization, which is not representative of a clinical setting. The
aim of this study was to investigate how well a DL model performs in unseen
clinical data sets---collected with different scanners, protocols and disease
populations---and whether more heterogeneous training data improves
generalization. In total, 3117 MRI scans of brains from multiple dementia
research cohorts and memory clinics, that had been visually rated by a
neuroradiologist according to Scheltens' scale of medial temporal atrophy
(MTA), were included in this study. By training multiple versions of a
convolutional neural network on different subsets of this data to predict MTA
ratings, we assessed the impact of including images from a wider distribution
during training had on performance in external memory clinic data. Our results
showed that our model generalized well to data sets acquired with similar
protocols as the training data, but substantially worse in clinical cohorts
with visibly different tissue contrasts in the images. This implies that future
DL studies investigating performance in out-of-distribution (OOD) MRI data need
to assess multiple external cohorts for reliable results. Further, by including
data from a wider range of scanners and protocols the performance improved in
OOD data, which suggests that more heterogeneous training data makes the model
generalize better. To conclude, this is the most comprehensive study to date
investigating the domain shift in deep learning on MRI data, and we advocate
rigorous evaluation of DL models on clinical data prior to being certified for
deployment.
