# A Deep Learning-based Compression and Classification Technique for Whole Slide Histopathology Images

arXiv: https://arxiv.org/abs/2305.07161 · HTML: https://mlsanity.com/paper/2305.07161/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 11 May 2023.

## Builds on (most cited first, 7 of 7)

- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [Context Autoencoder for Self-Supervised Representation Learning](https://mlsanity.com/paper/2202.03026/) (2022)
- [Decoder Denoising Pretraining for Semantic Segmentation](https://mlsanity.com/paper/2205.11423/) (2022)
- [Semantic Perceptual Image Compression using Deep Convolution Networks](https://mlsanity.com/paper/1612.08712/) (2016)
- [An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU](https://mlsanity.com/paper/1902.07385/) (2019)
- [Neural Image Compression and Explanation](https://mlsanity.com/paper/1908.08988/) (2019)

## Built on by (most cited first, 2 of 2)

- [WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression](https://mlsanity.com/paper/2503.18074/) (2025)
- [Deep learning-based compression of giga-resolution whole slide images](https://mlsanity.com/paper/2605.17668/) (2026)

## Abstract (arXiv)

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.