  This paper presents an autoencoder-based neural network architecture to
compress histopathological images while retaining the denser and more
meaningful representation of the original images. Current research into
improving compression algorithms is focused on methods allowing lower
compression rates for Regions of Interest (ROI-based approaches). Neural
networks are great at extracting meaningful semantic representations from
images, therefore are able to select the regions to be considered of interest
for the compression process. In this work, we focus on the compression of whole
slide histopathology images. The objective is to build an ensemble of neural
networks that enables a compressive autoencoder in a supervised fashion to
retain a denser and more meaningful representation of the input histology
images. Our proposed system is a simple and novel method to supervise
compressive neural networks. We test the compressed images using transfer
learning-based classifiers and show that they provide promising accuracy and
classification performance.
