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<p>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.</p>

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LazySlide: accessible and interoperable whole-slide image analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月20日

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  • Yimin Zheng,
  • Ernesto Abila,
  • Eva Chrenková,
  • Iva Buljan,
  • Juliane Winkler,
  • André F. Rendeiro

摘要

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.