Learning a low dimensional manifold of real cancer tissue with PathologyGAN

2020/04/13 by Adalberto Claudio Quiros, Quiros, Adalberto Claudio, Roderick Murray‐Smith +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2004.06517

openalex publication_date 2020/04/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Application of deep learning in digital pathology shows promise on improving disease diagnosis and understanding. 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. We study the latent space using 249K images from two breast cancer cohorts. We find that the latent space encodes morphological characteristics of tissues (e.g. patterns of cancer, lymphocytes, and stromal cells). In addition, the latent space reveals distinctly enriched clusters of tissue architectures in the high-risk patient group.

Citations

• Generative Adversarial Networks
• Precise Recovery of Latent Vectors from Generative Adversarial Networks
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Adversarial Autoencoders

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