  While challenging, the dense segmentation of histology images is a necessary
first step to assess changes in tissue architecture and cellular morphology.
Although specific convolutional neural network architectures have been applied
with great success to the problem, few effectively incorporate visual context
information from multiple scales. With this paper, we present a systematic
comparison of different architectures to assess how including multi-scale
information affects segmentation performance. A publicly available breast
cancer and a locally collected prostate cancer datasets are being utilised for
this study. The results support our hypothesis that visual context and scale
play a crucial role in histology image classification problems.
