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WSInsight as a cloud-native pipeline for single-cell pathology inference on whole-slide images

Huang, C. H.; Awosika, O. E.; Fernandez, D.

2025-12-10 bioinformatics
10.64898/2025.12.07.692260 bioRxiv
Show abstract

Digital pathology has increasingly embraced deep learning for large-scale tissue analysis, but most workflows remain limited by local execution and data accessibility.To address this challenge, we developed WSInsight, an open-source, cloud-native pipeline designed to enable scalable single-cell pathology inference directly on whole-slide images. The integration of distributed processing, cloud storage access, and automated quality control streamlines the application and reuse of advanced deep learning methods, expanding opportunities for diagnostic, prognostic, and spatially resolved research in digital pathology.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.