LazySlide: Accessible and interoperable whole slide image analysis

LazySlide: Accessible and interoperable whole slide image analysis#

LazySlide is a Python framework for whole slide image (WSI) analysis, designed to integrate seamlessly with the scverse ecosystem.

By adopting standardized data structures and APIs familiar to the single-cell and genomics community, LazySlide enables intuitive, interoperable, and reproducible workflows for histological analysis. It supports a range of tasks from basic preprocessing to advanced deep learning applications, facilitating the integration of histopathology into modern computational biology.

Key features#

Interoperability: Built on top of SpatialData, ensuring compatibility with scverse tools like Scanpy, Anndata, and Squidpy. Check out WSIData for more details.
Accessibility: User-friendly APIs that cater to both beginners and experts in digital pathology.
Scalability: Efficient handling of large WSIs, enabling high-throughput analyses.
Multimodal integration: Combine histological data with transcriptomics, genomics (omics data), and textual annotation.
Foundation model support: Native integration with state-of-the-art models (e.g., UNI, CONCH, Gigapath, Virchow) for tasks like zero-shot learning and captioning.
Deep learning ready: Provides PyTorch dataloaders for seamless integration into machine learning pipelines.

Whether you’re a novice in digital pathology or an expert computational biologist, LazySlide provides a scalable and modular foundation to accelerate AI-driven discovery in tissue biology and pathology.