  Medulloblastoma (MB) is the most common malignant brain tumor in childhood.
The diagnosis is generally based on the microscopic evaluation of
histopathological tissue slides. However, visual-only assessment of
histopathological patterns is a tedious and time-consuming task and is also
affected by observer variability. Hence, automated MB tumor classification
could assist pathologists by promoting consistency and robust quantification.
Recently, convolutional neural networks (CNNs) have been proposed for this
task, while transfer learning has shown promising results. In this work, we
propose an end-to-end MB tumor classification and explore transfer learning
with various input sizes and matching network dimensions. We focus on
differentiating between the histological subtypes classic and
desmoplastic/nodular. For this purpose, we systematically evaluate recently
proposed EfficientNets, which uniformly scale all dimensions of a CNN. Using a
data set with 161 cases, we demonstrate that pre-trained EfficientNets with
larger input resolutions lead to significant performance improvements compared
to commonly used pre-trained CNN architectures. Also, we highlight the
importance of transfer learning, when using such large architectures. Overall,
our best performing method achieves an F1-Score of 80.1%.
