  In this paper, we introduce a Variational Autoencoder (VAE) based training
approach that can compress and decompress cancer pathology slides at a
compression ratio of 1:512, which is better than the previously reported state
of the art (SOTA) in the literature, while still maintaining accuracy in
clinical validation tasks. The compression approach was tested on more common
computer vision datasets such as CIFAR10, and we explore which image
characteristics enable this compression ratio on cancer imaging data but not
generic images. We generate and visualize embeddings from the compressed latent
space and demonstrate how they are useful for clinical interpretation of data,
and how in the future such latent embeddings can be used to accelerate search
of clinical imaging data.
