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pathml

Automate pathology image analysis and multiparametric data workflows with Python.

Updated May 10, 2026
Search Tags:#deep learning#WSI#pathology#histology#dataset management#spatial analysis#multiparametric imaging
One-click install
npx skills add https://github.com/Imad-Oute/ResearchForge --skill pathml-imad-oute
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pathml
Source: https://github.com/Imad-Oute/ResearchForge/tree/main/OpenSource-Projects/claude-scientific-skills/scientific-skills/pathml
Command: npx skills add https://github.com/Imad-Oute/ResearchForge --skill pathml-imad-oute

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openslide, bioformats, PyTorch, h5py, dask, scanpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines and automates complex computational pathology tasks, enabling researchers and clinicians to analyze histology and multiplex imaging data efficiently through AI-driven workflows.

Core Features & Use Cases

  • Image Loading & Format Support: Facilitates access to over 160 slide formats, including WSI, DICOM, and multiplex images, simplifying data ingestion.
  • Image Preprocessing & Analysis: Offers modular pipelines for tissue detection, stain normalization, nuclei segmentation, and multiparametric data analysis, improving data quality and consistency.
  • Machine Learning & Spatial Graphs: Provides tools for training deep learning models such as HoVer-Net and HACTNet, and constructs spatial graphs for cellular interaction studies, supporting advanced research.
  • Data Management & Storage: Implements efficient large-scale dataset handling with HDF5, batching, and metadata organization for scalable workflows.
  • Application Scope: Ideal for projects in research, clinical diagnostics, and biomarker discovery involving histopathology images, spatial omics, and cellular phenotyping.

Quick Start

Start by installing PathML and running a tissue detection pipeline on your slide data with a few Python commands to load images, preprocess, and analyze cellular features interactively.

Frequently Asked Questions about pathml

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

FAQPage Schema
How do I analyze whole slide images for histopathology using deep learning?▼

Whole slide image analysis is automated through modular pipelines for tissue detection, stain normalization, and nuclei segmentation, supporting over 160 slide formats including DICOM and multiplex images for AI-driven research.

Can I train deep learning models like HoVer-Net for cellular phenotyping on multiplex imaging data?▼

Yes, deep learning model training for HoVer-Net and HACTNet is supported, alongside spatial graph construction for cellular interaction studies and multiparametric data analysis to enable advanced biomedical research.

What's the best way to manage large-scale pathology datasets efficiently?▼

Large-scale pathology datasets are managed efficiently using HDF5 for storage, batching, and metadata organization, allowing scalable workflows for handling high-resolution histology and spatial omics data.

Does this pathology workflow tool support spatial analysis for cellular interaction studies?▼

Spatial analysis is fully supported through the construction of spatial graphs for cellular interaction studies, integrating with multiparametric imaging workflows to facilitate advanced biomarker discovery and research.

Do I need PyTorch and OpenSlide to run tissue detection pipelines on WSI slides?▼

PyTorch and OpenSlide are required dependencies, along with bioformats, h5py, dask, and scanpy, providing the modular Python foundation for image processing, machine learning, and data management.

Are there limitations when preprocessing histology images with stain normalization across diverse formats?▼

Stain normalization and tissue detection pipelines support extensive format diversity, though end-to-end pathology solutions depend on modular Python libraries, requiring appropriate environment setup for consistent data quality.

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