  We propose a novel semi-supervised learning approach for classification of
histopathology images. We employ strong supervision with patch-level
annotations combined with a novel co-training loss to create a semi-supervised
learning framework. Co-training relies on multiple conditionally independent
and sufficient views of the data. We separate the hematoxylin and eosin
channels in pathology images using color deconvolution to create two views of
each slide that can partially fulfill these requirements. Two separate CNNs are
used to embed the two views into a joint feature space. We use a contrastive
loss between the views in this feature space to implement co-training. We
evaluate our approach in clear cell renal cell and prostate carcinomas, and
demonstrate improvement over state-of-the-art semi-supervised learning methods.
