TY - GEN
T1 - Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
AU - Fischer, Maximilian
AU - Neher, Peter
AU - Schüffler, Peter
AU - Xiao, Shuhan
AU - Almeida, Silvia Dias
AU - Ulrich, Constantin
AU - Muckenhuber, Alexander
AU - Braren, Rickmer
AU - Götz, Michael
AU - Kleesiek, Jens
AU - Nolden, Marco
AU - Maier-Hein, Klaus
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Digital Pathology
KW - Lossy Image Compression
KW - Whole Slide Imaging
UR - http://www.scopus.com/inward/record.url?scp=85175991886&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-45676-3_43
DO - 10.1007/978-3-031-45676-3_43
M3 - Conference contribution
AN - SCOPUS:85175991886
SN - 9783031456756
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 427
EP - 436
BT - Machine Learning in Medical Imaging - 14th International Workshop, MLMI 2023, Held in Conjunction with MICCAI 2023, Proceedings
A2 - Cao, Xiaohuan
A2 - Ouyang, Xi
A2 - Xu, Xuanang
A2 - Rekik, Islem
A2 - Cui, Zhiming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Workshop on Machine Learning in Medical Imaging, MLMI 2023
Y2 - 8 October 2023 through 8 October 2023
ER -