PathologyGAN: Learning deep representations of cancer tissue

Adalberto Claudio Quiros1, Roderick Murray-Smith1, Ke Yuan1
1: School of Computing Science, University of Glasgow
MIDL 2020 special issue
Publication date: 2021/03/22
PDF · arXiv · Video · Code


Histopathological images of tumours contain abundant information about how tumours grow and how they interact with their micro-environment. Better understanding of tissue phenotypes in these images could reveal novel determinants of pathological processes underlying cancer, and in turn improve diagnosis and treatment options. Advances of Deep learning makes it ideal to achieve those goals, however, its application is limited by the cost of high quality labels from patients data. Unsupervised learning, in particular, deep generative models with representation learning properties provides an alternative path to further understand cancer tissue phenotypes, capturing tissue morphologies. In this paper, we develop a framework which allows Generative Adversarial Networks (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 two different datasets, an H&E colorectal cancer tissue from the National Center for Tumor diseases (NCT, Germany) and an H&E breast cancer tissue from the Netherlands Cancer Institute (NKI, Netherlands) and Vancouver General Hospital (VGH, Canada). Composed of 86 slide images and 576 tissue micro-arrays (TMAs) respectively. We show that our model generates high quality images, with a Frechet Inception Distance (FID) of 16.65 (breast cancer) and 32.05 (colorectal cancer). 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


generative adversarial networks · digital pathology

Bibtex @article{melba:2021:004:quiros, title = "PathologyGAN: Learning deep representations of cancer tissue", author = "Quiros, Adalberto Claudio and Murray-Smith, Roderick and Yuan, Ke", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "MIDL 2020 special issue", year = "2021", issn = "2766-905X", url = "" }
RISTY - JOUR AU - Quiros, Adalberto Claudio AU - Murray-Smith, Roderick AU - Yuan, Ke PY - 2021 TI - PathologyGAN: Learning deep representations of cancer tissue T2 - Machine Learning for Biomedical Imaging VL - 1 IS - MIDL 2020 special issue SN - 2766-905X UR - ER -

2021:004 cover