Detect tissue in whole slide images and extract informative tiles for pathology analysis.
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill histolab-jasrajtulsi
Please help me install this Agent Skill. Skill: histolab Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/histolab Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill histolab-jasrajtulsi
Histolab reduces the manual burden of working with whole slide images by helping you detect tissue, inspect slide quality, normalize stain appearance, and extract useful tiles for downstream analysis.
Use the histolab skill to load a whole slide image, preview tissue masks and tile locations, and extract a small set of representative tiles for review.
High-intent search queries and answers about installing and using this skill.
You can extract tiles from whole slide images by using grid, random, or score-based tiling methods to select the most informative tissue regions. This process includes detecting tissue, applying masks to exclude background, and curating consistent image tiles for deep learning pathology datasets.
Tissue masking in digital pathology is the process of identifying and isolating actual tissue regions on a slide while excluding empty background and artifacts. It is needed to ensure that only relevant areas are processed and tiled, preventing wasted computation on non-informative regions during whole slide image analysis.
Yes, histolab processes OpenSlide-compatible whole slide images and requires OpenSlide access to load WSI files. It supports Python 3.8 to 3.11 and uses histolab interfaces for slides, masks, tilers, filters, and stain normalization to inspect slide metadata and dimensions.
Yes, you can normalize H&E stain appearance across whole slide images using histolab's stain normalization features. This allows you to standardize color and stain intensity variations before extracting tiles, ensuring consistent preprocessing for quality control and deep learning training datasets.
You can preview tissue masks and tile locations by loading a whole slide image and using histolab's visualization workflows to generate mask overlays and quality plots. This allows you to inspect tissue coverage, verify magnification, and review tile coordinates before actual extraction.
Automated tile extraction for digital pathology is limited by the quality of tissue masking and the presence of scanning artifacts that may require manual review. It also requires OpenSlide-compatible WSI access and Python 3.8 to 3.11, which may restrict usage in unsupported environments or with proprietary image formats.
galeepAutomates whole slide image processing for digital pathology.
dralkhConvert whole-slide and multiparametric pathology images into normalized tiles, masks, and model-ready datasets.
jackspaceExtract informative tiles and detect tissue regions from whole slide images.
SciMate-AIAutomate whole-slide image preprocessing and tile extraction for digital pathology workflows.
