  Medulloblastoma (MB) is a primary central nervous system tumor and the most
common malignant brain cancer among children. Neuropathologists perform
microscopic inspection of histopathological tissue slides under a microscope to
assess the severity of the tumor. This is a time-consuming task and often
infused with observer variability. Recently, pre-trained convolutional neural
networks (CNN) have shown promising results for MB subtype classification.
Typically, high-resolution images are divided into smaller tiles for
classification, while the size of the tiles has not been systematically
evaluated. We study the impact of tile size and input strategy and classify the
two major histopathological subtypes-Classic and Demoplastic/Nodular. To this
end, we use recently proposed EfficientNets and evaluate tiles with increasing
size combined with various downsampling scales. Our results demonstrate using
large input tiles pixels followed by intermediate downsampling and patch
cropping significantly improves MB classification performance. Our
top-performing method achieves the AUC-ROC value of 90.90\% compared to 84.53\%
using the previous approach with smaller input tiles.
