基于深度学习的全切片组织病理学图像压缩与分类技术
A Deep Learning-based Compression and Classification Technique for Whole Slide Histopathology Images
AI总结：
本文提出一种基于自编码器的监督压缩方法，用于全切片组织病理学图像，通过保留语义表示实现高效压缩，并验证了压缩图像在分类任务中的良好性能。
摘要：
本文提出了一种基于自编码器的神经网络架构，用于压缩组织病理学图像，同时保留原始图像更密集、更有意义的表示。当前改进压缩算法的研究集中于允许对感兴趣区域（基于ROI的方法）采用更低压缩率的方法。神经网络擅长从图像中提取有意义的语义表示，因此能够选择在压缩过程中被视为感兴趣的区域。在这项工作中，我们专注于全切片组织病理学图像的压缩。目标是构建一个神经网络集成，以监督方式实现压缩自编码器，从而保留输入组织学图像更密集、更有意义的表示。我们提出的系统是一种简单且新颖的监督压缩神经网络方法。我们使用基于迁移学习的分类器测试压缩图像，并表明它们提供了有前景的准确性和分类性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
