  Digital pathology tasks have benefited greatly from modern deep learning
algorithms. However, their need for large quantities of annotated data has been
identified as a key challenge. This need for data can be countered by using
unsupervised learning in situations where data are abundant but access to
annotations is limited. Feature representations learned from unannotated data
using contrastive predictive coding (CPC) have been shown to enable classifiers
to obtain state of the art performance from relatively small amounts of
annotated computer vision data. We present a modification to the CPC framework
for use with digital pathology patches. This is achieved by introducing an
alternative mask for building the latent context and using a multi-directional
PixelCNN autoregressor. To demonstrate our proposed method we learn feature
representations from the Patch Camelyon histology dataset. We show that our
proposed modification can yield improved deep classification of histology
patches.

Computer Vision and Pattern Recognition;Machine Learning