The paper introduces LazySlide, an open-source Python package designed to streamline the analysis of whole-slide images in histopathology. By integrating with the scverse ecosystem, the framework bridges the gap between traditional pathology and modern single-cell omics workflows. It utilizes vision-language foundation models to enable advanced features such as natural language querying, zero-shot classification, and automated tissue segmentation. Unlike existing tools, it employs a custom WSIData structure that allows for efficient, direct access to various proprietary image formats without heavy data duplication. Ultimately, LazySlide offers a user-friendly API that reduces coding complexity, making high-resolution biomedical imaging more accessible and interoperable for researchers.

References:

• Zheng Y, Abila E, Chrenková E, et al. LazySlide: accessible and interoperable whole-slide image analysis[J]. Nature Methods, 2026: 1-4.