Keighley, J orcid.org/0000-0002-3647-0683, de Kamps, M orcid.org/0000-0001-7162-4425, Wright, A et al. (1 more author) (2023) Digital pathology whole slide image compression with Vector Quantized Variational Autoencoders. In: Medical Imaging 2023: Digital and Computational Pathology. SPIE Medical Imaging 2023, 19-24 Feb 2023, San Diego, USA. SPIE
Abstract
Digital pathology Whole Slide Images (WSIs) are large images (∼30 GB/slide uncompressed) of high resolution (0.25 microns per pixel), presenting a significant data storage challenge for hospitals wishing to adopt digital pathology. Lossy compression has been adopted by scanner manufacturers to address this issue - we compare lossy Joint Photographic Experts Group (JPEG) compression for WSIs and investigate the Vector Quantised Variational Autoencoder 2 variant (VQVAE2) as a possible alternative to reduce file size while encoding useful features in the compressed representation. We trained three VQVAE2 models on a Camelyon 2016 subset to the Compression Ratio (CR) of 19.2:1 (CR1), 9.6:1 (CR2) and 4.8:1 (CR3) and tested on a Camelyon 2016 (DS1) subset; University of California (DS2) and Internal Validation Set (DS3). We then compared compression performance to ImageMagick JPEG and JPEG 2000 implementations. Both JPEG and JPEG 2000 compression outperformed the VQVAE2 implementation within the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics. The trained VQVAE2 models could visually reproduce WSI tissue structure, but used colours from the original training data within the reconstructions on other datasets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright 2023 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. |
Keywords: | Image compression; Pathology; Tissues; Scanners; Medical imaging; Quantization |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Mar 2023 16:10 |
Last Modified: | 19 Apr 2023 05:20 |
Status: | Published |
Publisher: | SPIE |
Identification Number: | 10.1117/12.2647844 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197145 |