  Deep learning based analysis of histopathology images shows promise in
advancing the understanding of tumor progression, tumor micro-environment, and
their underpinning biological processes. So far, these approaches have focused
on extracting information associated with annotations. In this work, we ask how
much information can be learned from the tissue architecture itself.
  We present an adversarial learning model to extract feature representations
of cancer tissue, without the need for manual annotations. We show that these
representations are able to identify a variety of morphological characteristics
across three cancer types: Breast, colon, and lung. This is supported by 1) the
separation of morphologic characteristics in the latent space; 2) the ability
to classify tissue type with logistic regression using latent representations,
with an AUC of 0.97 and 85% accuracy, comparable to supervised deep models; 3)
the ability to predict the presence of tumor in Whole Slide Images (WSIs) using
multiple instance learning (MIL), achieving an AUC of 0.98 and 94% accuracy.
  Our results show that our model captures distinct phenotypic characteristics
of real tissue samples, paving the way for further understanding of tumor
progression and tumor micro-environment, and ultimately refining
histopathological classification for diagnosis and treatment. The code and
pretrained models are available at:
https://github.com/AdalbertoCq/Adversarial-learning-of-cancer-tissue-representations
