Processing histopathological Whole Slide Images (WSI) leads to massive storage requirements for clinics worldwide. Even after lossy image compression during image acquisition, additional lossy compression is frequently possible without substantially affecting the performance of deep learning-based (DL) downstream tasks. In this paper, we show that the commonly used JPEG algorithm is not best suited for further compression and we propose Stain Quantized Latent Compression (SQLC ), a novel DL based histopathology data compression approach. SQLC compresses staining and RGB channels before passing it through a compression autoencoder (CAE ) in order to obtain quantized latent representations for maximizing the compression. We show that our approach yields superior performance in a classification downstream task, compared to traditional approaches like JPEG, while image quality metrics like the Multi-Scale Structural Similarity Index (MS-SSIM) is largely preserved. Our method is online available.

我们提出了一种基于深度学习的组织病理学数据压缩方法，通过压缩染色和RGB通道，并结合压缩自编码器来获得量化的潜在表示，从而在分类任务中展现出卓越的性能，同时基本保留了图像质量。

基于染色卷积的HE染色组织病理图像学习式压缩