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Application of deep learning in digital pathology shows promise on improvingdisease diagnosis and understanding. We present a deep generative model thatlearns to simulate high-fidelity cancer tissue images while mapping the realimages onto an interpretable low dimensional latent space. The key to the modelis an encoder trained by a previously developed generative adversarial network,PathologyGAN. We study the latent space using 249K images from two breastcancer cohorts. We find that the latent space encodes morphologicalcharacteristics of tissues (e.g. patterns of cancer, lymphocytes, and stromalcells). In addition, the latent space reveals distinctly enriched clusters oftissue architectures in the high-risk patient group.

Learning a low dimensional manifold of real cancer tissue with PathologyGAN