  Microscopic examination of tissues or histopathology is one of the diagnostic
procedures for detecting colorectal cancer. The pathologist involved in such an
examination usually identifies tissue type based on texture analysis,
especially focusing on tumour-stroma ratio. In this work, we automate the task
of tissue classification within colorectal cancer histology samples using deep
transfer learning. We use discriminative fine-tuning with one-cycle-policy and
apply structure-preserving colour normalization to boost our results. We also
provide visual explanations of the deep neural network's decision on texture
classification. With achieving state-of-the-art test accuracy of 96.2% we also
embark on using deployment friendly architecture called SqueezeNet for
memory-limited hardware.
