  Statistical analysis of magnetic resonance imaging (MRI) can help
radiologists to detect pathologies that are otherwise likely to be missed. Deep
learning (DL) has shown promise in modeling complex spatial data for brain
anomaly detection. However, DL models have major deficiencies: they need large
amounts of high-quality training data, are difficult to design and train and
are sensitive to subtle changes in scanning protocols and hardware. Here, we
show that also simple statistical methods such as voxel-wise (baseline and
covariance) models and a linear projection method using spatial patterns can
achieve DL-equivalent (3D convolutional autoencoder) performance in
unsupervised pathology detection. All methods were trained (N=395) and compared
(N=44) on a novel, expert-curated multiparametric (8 sequences) head MRI
dataset of healthy and pathological cases, respectively. We show that these
simple methods can be more accurate in detecting small lesions and are
considerably easier to train and comprehend. The methods were quantitatively
compared using AUC and average precision and evaluated qualitatively on
clinical use cases comprising brain atrophy, tumors (small metastases) and
movement artefacts. Our results demonstrate that while DL methods may be
useful, they should show a sufficiently large performance improvement over
simpler methods to justify their usage. Thus, simple statistical methods should
provide the baseline for benchmarks. Source code and trained models are
available on GitHub (https://github.com/vsaase/simpleBAD).
