Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
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Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
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
HACTNet - Hierarchical cell-type 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
# Install PathML
uv pip install pathml
# With optional dependencies for all features
uv pip install pathml[all]
Basic Workflow Example
from pathml.core import SlideData
from pathml.preprocessing import Pipeline, StainNormalizationHE, TissueDetectionHE
# Load a whole-slide image
wsi = SlideData.from_slide("path/to/slide.svs")
# Create preprocessing pipeline
pipeline = Pipeline([
TissueDetectionHE(),
StainNormalizationHE(target='normalize', stain_estimation_method='macenko')
])
# Run pipeline
pipeline.run(wsi)
# Access processed tilesfor tile in wsi.tiles:
processed_image = tile.image
tissue_mask = tile.masks['tissue']
Common Workflows
H&E Image Analysis:
Load WSI with appropriate slide class
Apply tissue detection and stain normalization
Perform nucleus detection or train segmentation models
Extract features and build spatial graphs
Conduct downstream analysis
Multiparametric Imaging (CODEX):
Load CODEX slide with CODEXSlide
Collapse multi-run channel data
Segment cells using Mesmer model
Quantify marker expression
Export to AnnData for single-cell analysis
Training ML Models:
Prepare dataset with public pathology data
Create PyTorch DataLoader with PathML datasets
Train HoVer-Net or custom models
Evaluate on held-out test sets
Deploy with ONNX for inference
References to Detailed Documentation
When working on specific tasks, refer to the appropriate reference file for comprehensive information:
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:
