Abstract
Accurate diagnosis of disease often depends on the exhaustive examination of Whole Slide Images (WSI) at microscopic resolution. Efficient handling of these data-intensive images requires lossy compression techniques. This paper investigates the limitations of the widely-used JPEG algorithm, the current clinical standard, and reveals severe image artifacts impacting diagnostic fidelity.
To overcome these challenges, we introduce a novel deep-learning (DL)-based compression method tailored for pathology images. By enforcing feature similarity of deep features between the original and compressed images, our approach achieves superior Peak Signal-to-Noise Ratio (PSNR), Multi-Scale Structural Similarity Index (MS-SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) scores compared to JPEG-XL, Webp, and other DL compression methods. Our method increases the PSNR value from 39 (JPEG80) to 41, indicating improved image fidelity and diagnostic accuracy.
Our approach can help to drastically reduce storage costs while maintaining large levels of image quality. Our method is online available.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ballé, J., Minnen, D., Singh, S., Hwang, S.J., Johnston, N.: Variational image compression with a scale hyperprior. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=rkcQFMZRb
Bégaint, J., Racapé, F., Feltman, S., Pushparaja, A.: CompressAI: a PyTorch library and evaluation platform for end-to-end compression research. arXiv preprint arXiv:2011.03029 (2020)
Borkowski, A.A., Bui, M.M., Thomas, L.B., Wilson, C.P., DeLand, L.A., Mastorides, S.M.: Lung and colon cancer histopathological image dataset (lc25000) (2019). https://doi.org/10.48550/ARXIV.1912.12142. https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images
Chen, Y., Janowczyk, A., Madabhushi, A.: Quantitative assessment of the effects of compression on deep learning in digital pathology image analysis. JCO Clin. Cancer Inf. 4, 221–233 (2020). https://doi.org/10.1200/CCI.19.00068
Ciga, O., Xu, T., Martel, A.L.: Self supervised contrastive learning for digital histopathology. Mach. Learn. Appl. 7, 100198 (2022). https://doi.org/10.1016/j.mlwa.2021.100198
Cover, T.M., Thomas, J.A.: Elements of information theory (2012)
Ghazvinian Zanjani, F., Zinger, S., Piepers, B., Mahmoudpour, S., Schelkens, P.: Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images. J. Med. Imaging 6(2), 1 (2019). https://doi.org/10.1117/1.JMI.6.2.027501
Ghouse, N.F.K.M., Petersen, J., Wiggers, A.J., Xu, T., Sautiere, G.: Neural image compression with a diffusion-based decoder (2023). https://openreview.net/forum?id=4Jq0XWCZQel
Kather, J.N., et al.: Multi-class texture analysis in colorectal cancer histology. Sci. Rep. 6, 27988 (2016). https://doi.org/10.1038/srep27988. https://www.kaggle.com/datasets/kmader/colorectal-histology-mnist
Mentzer, F., Toderici, G.D., Tschannen, M., Agustsson, E.: High-fidelity generative image compression. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 11913–11924. Curran Associates, Inc. (2020)
Pan, Z., Zhou, X., Tian, H.: Extreme generative image compression by learning text embedding from diffusion models. ArXiv abs/2211.07793 (2022)
Rippel, O., Anderson, A.G., Tatwawadi, K., Nair, S., Lytle, C., Bourdev, L.D.: ELF-VC: efficient learned flexible-rate video coding. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 14459–14468 (2021)
Russakovsky, O., Deng, J., Su, H., et al.: ImageNet large scale visual recognition challenge (2015). http://arxiv.org/abs/1409.0575
Song, M.S., Choi, J., Han, B.: Variable-rate deep image compression through spatially-adaptive feature transform. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2360–2369 (2021)
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016). https://doi.org/10.1109/TBME.2015.2496264
Theis, L., Shi, W., Cunningham, A., Huszár, F.: Lossy image compression with compressive autoencoders. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=rJiNwv9gg
Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment, vol. 2, pp. 1398–1402 (2003). https://doi.org/10.1109/ACSSC.2003.1292216
Wu, L., Huang, K., Shen, H.: A GAN-based tunable image compression system. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 2323–2331 (2020)
Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. Int. J. Comput. Vis. (IJCV) 127(8), 1106–1125 (2019)
Yang, R., Mandt, S.: Lossy image compression with conditional diffusion models. arXiv preprint arXiv:2209.06950 (2022)
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Acknowledgements
This work was partially supported by the DKTK Joint Funding UPGRADE, project “Subtyping of pancreatic cancer based on radiographic and pathological features” (SUBPAN), and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the grant 410981386.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fischer, M. et al. (2024). Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-45676-3_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45675-6
Online ISBN: 978-3-031-45676-3
eBook Packages: Computer ScienceComputer Science (R0)
