  In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation
(SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of
automatically identifying pathologies in brain images. Our work challenges the
effectiveness of current Machine Learning (ML) approaches in this application
domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR)
MR scans provides better anomaly segmentation maps than several different
ML-based anomaly detection models. Specifically, our method achieves better
Dice similarity coefficients and Precision-Recall curves than the competitors
on various popular evaluation data sets for the segmentation of tumors and
multiple sclerosis lesions.
