Accelerating Artificial Intelligence-based Whole Slide Image Analysis with an Optimized Preprocessing Pipeline
Abstract
As the field of digital pathology continues to advance, the computeraided analysis of whole slide images (WSI) has become an essential component for cancer diagnosis, staging, biomarker prediction, and therapy evaluation. However, even with the latest hardware developments, the processing of entire slides still demands significant computational resources. Therefore, many WSI analysis pipelines rely on patch-wise processing by tessellating a WSI into smaller sections and aggregating the results to retrieve slide-level outputs.One commonality among all these algorithms is the necessity for WSI preprocessing to extract patches, with each algorithm having its own requirements such as sliding window extraction or extracting patches at multiple magnification levels. In this paper, we present a novel Python-based software framework that leverages NVIDIA’s cuCIM library and parallelization to accelerate the preprocessing of WSIs, named PathoPatch. Compared to existing frameworks, we achieve a substantial reduction in processing time while maintaining or even improving the preprocessing capabilities. The code is available under https://github.com/TIO-IKIM/PathoPatcher.
Fabian Hörst
PhD Student
Jan Egger
Team Lead AI-guided Therapies
