Bibliographic Details
| Title: |
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning |
| Authors: |
Fischer, Maximilian, Neher, Peter, Schüffler, Peter, Xiao, Shuhan, Almeida, Silvia Dias, Ulrich, Constantin, Muckenhuber, Alexander, Braren, Rickmer, Götz, Michael, Kleesiek, Jens, Nolden, Marco, Maier-Hein, Klaus |
| Publication Year: |
2025 |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing |
| More Details: |
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. |
| Document Type: |
Working Paper |
| DOI: |
10.1007/978-3-031-45676-3_43 |
| Access URL: |
http://arxiv.org/abs/2503.11350 |
| Accession Number: |
edsarx.2503.11350 |
| Database: |
arXiv |
