Learned Image Compression for HE-stained Histopathological Images via Stain Deconvolution
This work addresses storage challenges for clinics handling histopathological images, though it is incremental as it builds on existing compression and deep learning techniques.
The paper tackles the massive storage requirements of histopathological Whole Slide Images by proposing a novel deep learning-based compression method called Stain Quantized Latent Compression (SQLC), which outperforms JPEG in classification downstream tasks while preserving image quality metrics like MS-SSIM.
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.