In this paper, we introduce a Variational Autoencoder (VAE) based training approach that can compress and decompress cancer pathology slides at a compression ratio of 1:512, which is better than the previously reported state of the art (SOTA) in the literature, while still maintaining accuracy in clinical validation tasks. The compression approach was tested on more common computer vision datasets such as CIFAR10, and we explore which image characteristics enable this compression ratio on cancer imaging data but not generic images. We generate and visualize embeddings from the compressed latent space and demonstrate how they are useful for clinical interpretation of data, and how in the future such latent embeddings can be used to accelerate search of clinical imaging data.

翻译：本文提出一种基于变分自编码器的训练方法，能够在1:512的压缩比下对癌症病理切片进行压缩与解压缩，该压缩比优于此前文献中报道的最先进水平，同时仍保持临床验证任务的准确性。该压缩方法在CIFAR10等常见计算机视觉数据集上进行了测试，并探究了哪些图像特征使得癌症影像数据能够实现这一压缩比而通用图像不能。我们生成并可视化来自压缩潜在空间的嵌入，论证其如何有助于数据的临床解读，以及未来如何利用此类潜在嵌入加速临床影像数据的检索。