  We present a self-supervised algorithm for several classification tasks
within hematoxylin and eosin (H&E) stained images of breast cancer. Our method
is robust to stain variations inherent to the histology images acquisition
process, which has limited the applicability of automated analysis tools. We
address this problem by imposing constraints a learnt latent space which
leverages stain normalization techniques during training. At every iteration,
we select an image as a normalization target and generate a version of every
image in the batch normalized to that target. We minimize the distance between
the embeddings that correspond to the same image under different staining
variations while maximizing the distance between other samples. We show that
our method not only improves robustness to stain variations across multi-center
data, but also classification performance through extensive experiments on
various normalization targets and methods. Our method achieves the
state-of-the-art performance on several publicly available breast cancer
datasets ranging from tumor classification (CAMELYON17) and subtyping (BRACS)
to HER2 status classification and treatment response prediction.
