  The field of computational pathology presents many challenges for computer
vision algorithms due to the sheer size of pathology images. Histopathology
images are large and need to be split up into image tiles or patches so modern
convolutional neural networks (CNNs) can process them. In this work, we present
a method to generate an interpretable image representation of computational
pathology images using quadtrees and a pipeline to use these representations
for highly accurate downstream classification. To the best of our knowledge,
this is the first attempt to use quadtrees for pathology image data. We show it
is highly accurate, able to achieve as good results as the currently widely
adopted tissue mask patch extraction methods all while using over 38% less
data.
