We apply Generative Adversarial Networks (GANs) to the domain of digital
pathology. Current machine learning research for digital pathology focuses on
diagnosis, but we suggest a different approach and advocate that generative
models could drive forward the understanding of morphological characteristics
of cancer tissue. In this paper, we develop a framework which allows GANs to
capture key tissue features and uses these characteristics to give structure to
its latent space. To this end, we trained our model on 249K H&E breast cancer
tissue images, extracted from 576 TMA images of patients from the Netherlands
Cancer Institute (NKI) and Vancouver General Hospital (VGH) cohorts. We show
that our model generates high quality images, with a Frechet Inception Distance
(FID) of 16.65. We further assess the quality of the images with cancer tissue
characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using
quantitative information to calculate the FID and showing consistent
performance of 9.86. Additionally, the latent space of our model shows an
interpretable structure and allows semantic vector operations that translate
into tissue feature transformations. Furthermore, ratings from two expert
pathologists found no significant difference between our generated tissue
images from real ones. The code, generated images, and pretrained model are
available at https://github.com/AdalbertoCq/Pathology-GANComment: MIDL 2020 final versio