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pathml

Load, preprocess, and analyze whole-slide images and multiparametric datasets.

1|1|Updated Mar 13, 2026
Search Tags:#machine learning#image analysis#whole slide imaging#multiplex imaging#computational pathology#spatial biology
One-click install
npx skills add https://github.com/youyinnn/skills-collection --skill pathml-youyinnn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pathml
Source: https://github.com/youyinnn/skills-collection/tree/main/plugins/data-preparation-and-processing/skills/pathml
Command: npx skills add https://github.com/youyinnn/skills-collection --skill pathml-youyinnn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines complex computational pathology workflows, enabling advanced analysis of whole-slide images and multiparametric data.

Core Features & Use Cases

  • Image Loading: Supports 160+ WSI formats (Aperio, Hamamatsu, Leica, etc.).
  • Preprocessing: Stain normalization, nucleus segmentation, artifact detection.
  • Graph Construction: Builds cell and tissue graphs for spatial analysis.
  • Machine Learning: Integrates pre-trained models (HoVer-Net) and training pipelines.
  • Multiparametric Analysis: Handles CODEX, Vectra, MERFISH data.
  • Use Case: Analyze multiplex immunofluorescence images to identify spatial relationships between immune cells and tumor cells, quantifying their interactions to predict treatment response.

Quick Start

Use the pathml skill to load the whole-slide image located at /path/to/slide.svs and generate tiles.

Frequently Asked Questions about pathml

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

FAQPage Schema
How do I load and preprocess whole-slide images for computational pathology?▼

A computational pathology toolkit streamlines whole-slide image preprocessing by loading 160+ WSI formats, applying stain normalization, and performing nucleus segmentation to generate analysis-ready image tiles.

Can I analyze spatial relationships in multiplex immunofluorescence images?▼

Yes, spatial relationships in multiplex imaging data from CODEX, Vectra, and MERFISH datasets can be analyzed by constructing cell and tissue graphs to quantify interactions between immune and tumor cells for treatment response prediction.

Does this computational pathology workflow support pre-trained machine learning models?▼

The computational pathology workflow supports machine learning by integrating pre-trained models like HoVer-Net and PyTorch training pipelines to perform nucleus segmentation on whole-slide images.

What is the best way to build spatial graphs from multiparametric imaging data?▼

Building spatial graphs from multiparametric imaging data requires a computational pathology toolkit to construct cell and tissue graphs from segmented nuclei, enabling spatial biology analysis of cellular interactions.

Do I need OpenSlide to process whole-slide imaging files in multiple formats?▼

You need OpenSlide integration to load and process over 160 whole-slide imaging formats like Aperio and Hamamatsu, enabling computational pathology workflows to access multi-resolution image data.

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