histolab

Name: histolab
Availability: InStock
Author: Engstrom

Automate preprocessing and analysis of whole-slide images for digital pathology workflows.

18|1|Updated Dec 27, 2025

Search Tags:#visualization #histolab #pathology #tile-extraction #tissue-mask #whole-slide-image

Ready to run

npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill histolab-logauaengstrom

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: histolab
Source: https://github.com/LogauaEngstrom/claude-scientific-skills/tree/main/scientific-skills/histolab
Command: npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill histolab-logauaengstrom

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Histolab automates preprocessing and analysis of whole-slide images, enabling consistent tissue detection, tile extraction, and dataset preparation for deep learning pipelines.

Core Features & Use Cases

Slide management and metadata extraction for WSIs (SVS, TIFF, NDPI)
Tissue masks and tile extraction across multiple strategies
Visualization and reporting for quality control and reproducibility
Workflow support for end-to-end pathology research pipelines

Quick Start

Install histolab, load a slide, configure a tiler, and run tile extraction.

Frequently Asked Questions about histolab

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

FAQPage Schema

How do I automate tissue detection and tile extraction from whole-slide images?▼

Whole-slide image tile extraction applies tissue masks and multiple tiling strategies to isolate tissue regions, producing consistent tiles for deep learning dataset preparation across SVS, TIFF, and NDPI formats.

What is the best way to prepare whole-slide image datasets for deep learning in digital pathology?▼

Whole-slide image dataset preparation automates slide management, metadata extraction, and tile extraction to generate consistent tissue tiles, producing reproducible deep learning training datasets for digital pathology workflows.

Does histolab support processing SVS, TIFF, and NDPI whole-slide image formats?▼

Yes, whole-slide image processing supports SVS, TIFF, and NDPI formats, automating slide management and metadata extraction to enable tissue detection and tiling for pathology research pipelines.

How do I generate tissue masks and visualize quality control for whole-slide images?▼

Generating tissue masks and visualizing quality control applies automated tissue detection to whole-slide images, producing masks and reports that ensure reproducibility and support quality control in pathology workflows.

Can I extract tiles from whole-slide images using multiple tiling strategies?▼

Yes, tile extraction from whole-slide images supports multiple tiling strategies, allowing you to configure a tiler and apply different extraction methods to generate diverse tile datasets for deep learning pipelines.

Why do I need YAML frontmatter to run whole-slide image processing tasks?▼

YAML frontmatter with name and description is required for whole-slide image processing tasks to support discovery and execution, ensuring proper task identification and enabling optional resources like scripts or references.