LazySlide: accessible and interoperable whole-slide image analysis

Nature Methods 2026 Open access

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Authors: Yimin Zheng, Ernesto Abila, Eva Chrenková, Iva Buljan, Juliane Winkler, André F. Rendeiro
DOI: 10.1038/s41592-026-03044-7
Cited by: 2
References: 33

Abstract

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.

Fields of Study & Subjects

Cell Image Analysis Techniques Advanced Image and Video Retrieval Techniques Computer Graphics and Visualization Techniques

Publication details

Journal / source
Nature Methods
Publisher
Springer Science and Business Media LLC
Volume
23
Issue
4
Pages
728–731
Publication date
20 March 2026
Language
EN