Pathology GAN: Learning deep representations of cancer tissue
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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 help to understand and find fundamental morphological characteristics of cancer tissue. In this paper, we develop a framework which allows GANs to capture key tissue features, and present a vision of how these could link cancer tissue and DNA in the future. To this end, we trained our model on breast cancer tissue from a medium size cohort of 526 patients, producing high fidelity images. We further study how a range of relevant GAN evaluation metrics perform on this task, and propose to evaluate synthetic images with clinically/pathologically meaningful features. Our results show that these models are able to capture key morphological characteristics that link with phenotype, such as survival time and Estrogen-receptor (ER) status. Using an Inception-V1 network as feature extraction, our models achieve a Frechet Inception Distance (FID) of 18.4. We find that using pathology meaningful features on these metrics show consistent performance, with a FID of 8.21. Furthermore, we asked two expert pathologists to distinguish our generated images from real ones, finding no significant difference between them.
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