  Convolutional neural networks can be trained to perform histology slide
classification using weak annotations with multiple instance learning (MIL).
However, given the paucity of labeled histology data, direct application of MIL
can easily suffer from overfitting and the network is unable to learn rich
feature representations due to the weak supervisory signal. We propose to
overcome such limitations with a two-stage semi-supervised approach that
combines the power of data-efficient self-supervised feature learning via
contrastive predictive coding (CPC) and the interpretability and flexibility of
regularized attention-based MIL. We apply our two-stage CPC + MIL
semi-supervised pipeline to the binary classification of breast cancer
histology images. Across five random splits, we report state-of-the-art
performance with a mean validation accuracy of 95% and an area under the ROC
curve of 0.968. We further evaluate the quality of features learned via CPC
relative to simple transfer learning and show that strong classification
performance using CPC features can be efficiently leveraged under the MIL
framework even with the feature encoder frozen.
