Learning a Low Dimensional Manifold of Real Cancer Tissue with PathologyGAN

Claudio Quiros, A., Murray-Smith, R. and Yuan, K. (2020) Learning a Low Dimensional Manifold of Real Cancer Tissue with PathologyGAN. NeurIPS 2020 Learning Meaningful Representations of Life, 11 Dec 2020.

Abstract

Histopathological images contain information about how a tumor interacts with its micro-environment. Better understanding of such interaction holds the key for improved diagnosis and treatment of cancer. Deep learning shows promise on achieving those goals, however, its application is limited by the cost of high quality labels. Unsupervised learning, in particular, deep generative models with representation learning properties provides an alternative path to further understand cancer tissue phenotypes, capturing tissue morphologies. We present a deep generative model that learns to simulate high-fidelity cancer tissue images while mapping the real images onto an interpretable low dimensional latent space. The key to the model is an encoder trained by a previously developed generative adversarial network, PathologyGAN. Here we provide examples of how the latent space holds morphological characteristics of cancer tissue (e.g. tissue type or cancer, lymphocytes, and stroma cells). We tested the general applicability of our representations in three different settings: latent space visualization, training a tissue type classifier over latent representations, and on multiple instance learning (MIL). Latent visualizations of breast cancer tissue show that distinct regions of the latent space enfold different characteristics (stroma, lymphocytes, and cancer cells). A logistic regression for colorectal tissue type classification trained over latent projections achieves 87% accuracy. Finally, we used the attention-based deep MIL for predicting presence of epithelial cells in colorectal tissue, achieving 90% accuracy. Our results show that PathologyGAN captures distinct phenotype characteristics, paving the way for further understanding of tumor micro-environment and ultimately refining histopathological classification for diagnosis and treatment.

• Item Type: Conference or Workshop Item
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor Roderick and Yuan, Dr Ke and Claudio Quiros, Mr Adalberto
• Authors: Claudio Quiros, A., Murray-Smith, R., and Yuan, K.
• College/School: College of Science and Engineering > School of Computing Science

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