Abstract: Unlocking the Potential of Digital Pathology - Novel Baselines for Compression

Maximilian Fischer, Peter Neher, Peter Schüffler, Sebastian Ziegler, Shuhan Xiao, Robin Peretzke, David A. Clunie, Constantin Ulrich, Michael Baumgartner, Alexander Muckenhuber, Silvia Dias Almeida, Michael Götz, Jens Kleesiek, Marco Nolden, Rickmer Braren, Klaus H. Maier-Hein

Abstract: Digital pathology offers the opportunity to transform clinical practice in histopathological image analysis, yet faces a significant hurdle: the substantial file sizes of pathological WSIs. Whereas current digital pathology solutions rely on lossy JPEG compression to address this issue, lossy compression can introduce color and texture disparities, potentially impacting clinical decision-making. Prior research addresses perceptual image quality and downstream performance independently of each other, we jointly evaluate compression schemes for perceptual and downstream task quality on four different datasets. In addition, we collect an initially uncompressed dataset for an unbiased perceptual evaluation of compression schemes. Our results show that deep learning models fine-tuned for perceptual quality outperform conventional compression schemes like JPEG-XL or WebP for further compression of WSI. We introduce a novel evaluation metric based on feature similarity between original files and compressed files that aligns with the downstream performance on the compressedWSI. Our study provides novel insights for the assessment of lossy compression schemes forWSI and encourages a unified evaluation of lossy compression schemes to accelerate the clinical uptake of digital pathology [1].

External IDs:dblp:conf/bildmed/FischerNSZXPCUB26