pathml

Analyze whole-slide images and train deep learning models for pathology.

8|Updated Jan 13, 2026
One-click install
npx skills add https://github.com/hxk622/TokenDance --skill pathml-hxk622
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: pathml
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/clinical/pathml
Command: npx skills add https://github.com/hxk622/TokenDance --skill pathml-hxk622

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 for research and clinical applications.

Core Features & Use Cases

  • WSI Analysis: Load and process diverse whole-slide image formats.
  • ML Model Training: Train and deploy deep learning models for nucleus segmentation and classification.
  • Multiparametric Imaging: Analyze spatial proteomics data from CODEX, Vectra, and MERFISH.
  • Use Case: Researchers can use this Skill to segment nuclei, quantify protein expression in thousands of cells across multiple tissue samples, and build spatial graphs for machine learning analysis.

Quick Start

Use the pathml skill to load the whole-slide image 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 process whole-slide images for computational pathology analysis?

Whole-slide image analysis supports loading and processing over 160 diverse slide formats for research and clinical applications. You can generate tiles from whole-slide images to prepare complex pathology data for downstream machine learning workflows.

Can I train deep learning models for nucleus segmentation and classification?

You can train and deploy deep learning models for nucleus segmentation and classification. The toolkit integrates with deep learning frameworks to process complex pathology data and build ML models for advanced tissue analysis.

Does this toolkit support multiplexed immunofluorescence and spatial proteomics data?

Multiparametric imaging analysis supports spatial proteomics data from CODEX, Vectra, and MERFISH platforms. You can quantify protein expression across thousands of cells in multiple tissue samples and build spatial graphs for machine learning analysis.

What is the best way to construct tissue graphs for machine learning analysis?

Tissue graph construction is built directly into the computational pathology workflow, allowing you to build spatial graphs from segmented nuclei and quantified protein expression data for advanced machine learning analysis.

Do I need specific whole-slide image formats to run WSI analysis?

WSI analysis does not restrict you to specific formats, as the toolkit supports loading and processing over 160 diverse whole-slide image formats for both research and clinical pathology applications.