histolab

Extract informative tiles and detect tissue regions from whole slide images.

16|7|Updated Nov 20, 2025
One-click install
npx skills add https://github.com/jackspace/ClaudeSkillz --skill histolab
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: histolab
Source: https://github.com/jackspace/ClaudeSkillz/tree/main/skills/scientific-pkg-histolab
Command: npx skills add https://github.com/jackspace/ClaudeSkillz --skill histolab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires histolab, and includes references (resource) components.

What problem does it solve?

Histolab enables digital pathology workflows: extract tiles from gigapixel WSIs, detect tissue, generate masks, and prepare datasets for deep learning.

Core Features & Use Cases

  • Slide management across WSI formats (SVS, TIFF, NDPI)
  • Tissue detection and masks
  • Tile extraction strategies (Random, Grid, Score-based)
  • Visualization and preprocessing for ML pipelines

Quick Start

Install histolab and run a basic tile extraction on a sample slide.

Frequently Asked Questions about histolab

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract tiles from whole slide images for deep learning?

Tile extraction from WSIs involves partitioning gigapixel pathology images into smaller regions for analysis. Histolab automates this using multiple strategies—Random, Grid, or Score-based selection—to generate datasets from SVS, TIFF, and NDPI formats compatible with deep learning pipelines.

Can I detect and mask tissue regions in H&E and IHC stained slides?

Tissue detection and masking identify stained regions within WSIs, separating meaningful pathology data from background. Histolab applies automated tissue masking across multiple stain types and WSI formats to prepare clean inputs for downstream analysis.

What's the best way to process gigapixel pathology images at scale?

Processing gigapixel WSIs requires handling pyramidal image structures and managing memory efficiently. Histolab provides slide management, metadata access, and tile extraction across formats to handle large-scale digital pathology workflows without loading entire images into memory.

Does histolab support multiple WSI file formats?

WSI preprocessing requires compatibility across vendor formats. Histolab supports SVS, TIFF, and NDPI formats, enabling unified tile extraction and tissue analysis regardless of acquisition platform.

How do I prepare histopathology datasets for machine learning?

Dataset preparation for pathology ML involves tissue detection, tile selection, and format standardization. Histolab automates these preprocessing steps—masking tissue, extracting informative tiles, and managing slide metadata—to create analysis-ready datasets.