Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution

TL;DR

This paper introduces SQLC, a deep learning-based compression method tailored for histopathological images, which outperforms JPEG in preserving image quality and classification accuracy while achieving higher compression rates.

Contribution

The paper presents SQLC, a novel DL-based compression technique that specifically targets stain and RGB channels for better compression of histopathology images.

Findings

SQLC outperforms JPEG in classification tasks.

MS-SSIM scores are largely preserved with SQLC.

SQLC achieves higher compression ratios.

Abstract

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.