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LazySlide: accessible and interoperable whole slide image analysis

Zheng, Y.; Abila, E.; Chrenkova, E.; Winkler, J.; Rendeiro, A. F.

2025-06-01 bioinformatics
10.1101/2025.05.28.656548 bioRxiv
Show abstract

Histopathological data are foundational in both biological research and clinical diagnostics but remain siloed from modern multimodal and single-cell frameworks. We introduce LazySlide, an open-source Python package built on the scverse ecosystem for efficient whole-slide image (WSI) analysis and multimodal integration. By leveraging vision-language foundation models and adhering to scverse data standards, LazySlide bridges histopathology with omics workflows. It supports tissue and cell segmentation, feature extraction, cross-modal querying, and zero-shot classification, with minimal setup. Its modular design empowers both novice and expert users, lowering the barrier to advanced histopathology analysis and accelerating AI-driven discovery in tissue biology and pathology.

Published in Nature Methods (predicted rank #1) · training set

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