Histolab

If you're working with whole slide images in digital pathology, this handles the annoying groundwork: tissue detection, tile extraction, and H&E preprocessing without writing a bunch of boilerplate. It wraps OpenSlide with three solid extraction strategies (random sampling, grid coverage, or score-based selection using nuclei/cellularity metrics), which is exactly what you need for prepping training datasets. The API is clean and the tile preview functions are genuinely useful before you burn compute extracting from gigapixel images. Python 3.8-3.11 only, Linux or macOS, and you'll need OpenSlide system libraries installed first. It's positioned as the lightweight option compared to pathml, so if you just need tiles out of slides without complex spatial proteomics pipelines, this is the faster path.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill histolab --agent claude-code

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Files

Histolab

When to use

Use Histolab to inspect WSI metadata, identify tissue, extract image tiles, and standardize H&E staining. Its masks and scores are image-processing heuristics; they do not diagnose cancer, count individual cells, or establish image quality.

Installation

Histolab 0.7.0 remains the latest published release as of the review date. Its release constraints require Python <3.12, NumPy <=1.24.4, scikit-image <0.19.4, SciPy <1.10.1, Pillow <11, and openslide-python 1.3.1. Keep this stack isolated from modern scientific environments. Windows is not supported by this Histolab release.

Install native OpenSlide for your system, then create a dedicated environment (Python 3.10 was tested):

uv venv --python 3.10 .venv-histolab
uv pip install --python .venv-histolab/bin/python 'histolab==0.7.0' pooch matplotlib
.venv-histolab/bin/python -c 'import openslide; print(openslide.__library_version__)'

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