  White Matter Hyperintensities (WMH) are areas of the brain that have higher
intensity than other normal brain regions on Magnetic Resonance Imaging (MRI)
scans. WMH is often associated with small vessel disease in the brain, making
early detection of WMH important. However, there are two common issues in the
detection of WMH: high ambiguity and difficulty in detecting small WMH. In this
study, we propose a method called Probabilistic TransUNet to address the
precision of small object segmentation and the high ambiguity of medical
images. To measure model performance, we conducted a k-fold cross validation
and cross dataset robustness experiment. Based on the experiments, the addition
of a probabilistic model and the use of a transformer-based approach were able
to achieve better performance.
