Processes and prepares gigapixel whole slide images for digital pathology and machine learning workflows.
Histolab is a specialized Claude Code Skill designed for computational pathology researchers and developers working with massive whole slide images (WSI). It streamlines the complex process of handling gigapixel medical data by providing automated tissue detection, sophisticated mask generation, and diverse tile extraction strategies. Supporting formats like SVS, TIFF, and NDPI, this skill bridges the gap between raw histological slides and production-ready deep learning pipelines, enabling users to efficiently sample informative regions while filtering out artifacts and background noise.
Key Features
01Support for major gigapixel WSI formats such as SVS, TIFF, and NDPI
02Advanced image filtering for tissue enhancement and pen annotation removal
03Integrated visualization tools for slide thumbnails and tile distribution analysis
04Multi-strategy tile extraction including Random, Grid, and Score-based tiling
0581 GitHub stars
06Automated tissue detection and sophisticated binary mask generation
Use Cases
01Performing spatial analysis and systematic grid-based slide scanning
02Preparing large-scale datasets for histopathology deep learning training
03Automating tissue region segmentation and background exclusion for research
