A deep learning-based compression and classification technique for whole slide histopathology images
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 and, therefore can 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.
History
Refereed
- Yes
Volume
16Page range
4517-4526Publication title
International Journal of Information TechnologyISSN
2511-2104External DOI
Publisher
Springer Science and Business Media LLCFile version
- Published version
Language
- eng
Item sub-type
Journal ArticleOfficial URL
Affiliated with
- School of Computing and Information Science Outputs