Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile
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Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile
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LanguagePython
LicenseMIT
📦 Ships with scientific-agent-skills
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pathml.SKILL.md
---name: pathml
description: Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.
license: GPL-2.0 license
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---# PathML
## Overview
PathML is a comprehensive Python toolkit for computational pathology workflows, designed to facilitate machine learning and image analysis for whole-slide pathology images. The framework provides modular, composable tools for loading diverse slide formats, preprocessing images, constructing spatial graphs, training deep learning models, and analyzing multiparametric imaging data from technologies like CODEX and multiplex immunofluorescence.
## When to Use This Skill
Apply this skill for:
- Loading and processing whole-slide images (WSI) in various proprietary formats
- Preprocessing H&E stained tissue images with stain normalization
- Nucleus detection, segmentation, and classification workflows
- Building cell and tissue graphs for spatial analysis
- Training or deploying machine learning models (HoVer-Net, HACTNet) on pathology data
- Analyzing multiparametric imaging (CODEX, Vectra, MERFISH) for spatial proteomics
- Quantifying marker expression from multiplex immunofluorescence
- Managing large-scale pathology datasets with HDF5 storage
- Tile-based analysis and stitching operations
## Core Capabilities
PathML provides six major capability areas documented in detail within reference files:
### 1. Image Loading & Formats
Load whole-slide images from 160+ proprietary formats including Aperio SVS, Hamamatsu NDPI, Leica SCN, Zeiss ZVI, DICOM, and OME-TIFF. PathML automatically handles vendor-specific formats and provides unified interfaces for accessing image pyramids, metadata, and regions of interest.
**See:** `references/image_loading.md` for supported formats, loading strategies, and working with different slide types.
### 2. Preprocessing Pipelines
Build modular preprocessing pipelines by composing transforms for image manipulation, quality control, stain normalization, tissue detection, and mask operations. PathML's Pipeline architecture enables reproducible, scalable preprocessing across large datasets.
- `LabelArtifactTileHE` - Quality control for artifacts
**See:** `references/preprocessing.md` for complete transform catalog, pipeline construction, and preprocessing workflows.
### 3. Graph Construction
Construct spatial graphs representing cellular and tissue-level relationships. Extract features from segmented objects to create graph-based representations suitable for graph neural networks and spatial analysis.
**See:** `references/graphs.md` for graph construction methods, feature extraction, and spatial analysis workflows.
### 4. Machine Learning
Train and deploy deep learning models for nucleus detection, segmentation, and classification. PathML integrates PyTorch with pre-built models (HoVer-Net, HACTNet), custom DataLoaders, and ONNX support for inference.
**Key models:**
- **HoVer-Net** - Simultaneous nucleus segmentation and classification
**See:** `references/machine_learning.md` for model training, evaluation, inference workflows, and working with public datasets.
### 5. Multiparametric Imaging
Analyze spatial proteomics and gene expression data from CODEX, Vectra, MERFISH, and other multiplex imaging platforms. PathML provides specialized slide classes and transforms for processing multiparametric data, cell segmentation with Mesmer, and quantification workflows.
**See:** `references/multiparametric.md` for CODEX/Vectra workflows, cell segmentation, marker quantification, and integration with AnnData.
### 6. Data Management
Efficiently store and manage large pathology datasets using HDF5 format. PathML handles tiles, masks, metadata, and extracted features in unified storage structures optimized for machine learning workflows.
**See:** `references/data_management.md` for HDF5 integration, tile management, dataset organization, and batch processing strategies.
## Quick Start
### Installation
```bash
# Install PathML
uv pip install pathml
# With optional dependencies for all features
uv pip install pathml[all]
```
### Basic Workflow Example
```python
from pathml.core import SlideData
from pathml.preprocessing import Pipeline, StainNormalizationHE, TissueDetectionHE
This skill includes comprehensive reference documentation organized by capability area. Each reference file contains detailed API information, workflow examples, best practices, and troubleshooting guidance for specific PathML functionality.
### references/
Documentation files providing in-depth coverage of PathML capabilities:
