  Early detection of dementia through specific biomarkers in MR images plays a
critical role in developing support strategies proactively. Fazekas scale
facilitates an accurate quantitative assessment of the severity of white matter
lesions and hence the disease. Imaging Biomarkers of dementia are multiple and
comprehensive documentation of them is time-consuming. Therefore, any effort to
automatically extract these biomarkers will be of clinical value while reducing
inter-rater discrepancies. To tackle this problem, we propose to classify the
disease severity based on the Fazekas scale through the visual biomarkers,
namely the Periventricular White Matter (PVWM) and the Deep White Matter (DWM)
changes, in the real-world setting of thick-slice MRI. Small training sample
size and weak supervision in form of assigning severity labels to the whole MRI
stack are among the main challenges. To combat the mentioned issues, we have
developed a deep learning pipeline that employs self-supervised representation
learning, multiple instance learning, and appropriate pre-processing steps. We
use pretext tasks such as non-linear transformation, local shuffling, in- and
out-painting for self-supervised learning of useful features in this domain.
Furthermore, an attention model is used to determine the relevance of each MRI
slice for predicting the Fazekas scale in an unsupervised manner. We show the
significant superiority of our method in distinguishing different classes of
dementia compared to state-of-the-art methods in our mentioned setting, which
improves the macro averaged F1-score of state-of-the-art from 61% to 76% in
PVWM, and from 58% to 69.2% in DWM.
