Histopathological data are foundational in both biological research and clinical diagnostics but remain siloed from modern multimodal and single-cell frameworks. Here we introduce LazySlide, an open-source Python package built on the scverse ecosystem for efficient whole-slide image 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. LazySlide combines scverse with foundation models to enable efficient whole-slide image analysis.
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Zheng et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69bf8692f665edcd009e8dff — DOI: https://doi.org/10.1038/s41592-026-03044-7
Yimin Zheng
Ernesto Abila
Eva Chrenková
Nature Methods
Medical University of Vienna
Austrian Academy of Sciences
Comprehensive Cancer Center Vienna
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