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.

翻译：本文提出了一种基于自编码器的神经网络架构，用于在保留原始图像更密集、更有意义的表征的同时压缩组织病理学图像。当前改进压缩算法的研究主要聚焦于允许对感兴趣区域采用较低压缩率的方法（基于感兴趣区域的方法）。由于神经网络擅长从图像中提取有意义的语义表征，因此能够选择在压缩过程中被视为感兴趣的区域。本研究重点探讨全切片组织病理学图像的压缩问题，目标是通过构建神经网络集成，以监督方式驱动压缩自编码器保留输入组织学图像中更密集、更有意义的表征。我们提出的系统是一种简单且新颖的监督式压缩神经网络方法。通过基于迁移学习的分类器对压缩图像进行测试，结果表明该方法在准确率和分类性能方面具有良好表现。