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

Does NOT Trigger

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.

Key transforms:

• StainNormalizationHE - Macenko/Vahadane stain normalization
• TissueDetectionHE, NucleusDetectionHE - Tissue/nucleus segmentation
• MedianBlur, GaussianBlur - Noise reduction
• 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

PathML needs native dependencies present first — OpenSlide and a JDK (Bio-Formats is driven through JPype/javabridge) — so the maintainers recommend a conda environment; pure-pip installs commonly fail on those native deps.

Give PathML its own environment, because 3.0.8 pins much of the scientific stack to exact or upper-bounded versions:

Installing PathML next to alterlab-scanpy or alterlab-squidpy-spatial will either fail to resolve or silently downgrade those skills' stack. Move results between environments as files (HDF5/AnnData written by PathML, read by a current-scanpy env) rather than trying to satisfy both pin sets.

# In a dedicated env, with OpenSlide and a JDK already installed.
uv venv .venv-pathml && source .venv-pathml/bin/activate
uv pip install pathml

Basic Workflow Example

from pathml.core import HESlide
from pathml.preprocessing import Pipeline, StainNormalizationHE, TissueDetectionHE

# Load a whole-slide image. Use the HESlide convenience class for H&E,
# or SlideData(filepath=..., slide_type=types.HE) for the generic constructor.
# (There is no SlideData.from_slide.)
wsi = HESlide("path/to/slide.svs", name="example")

# Create preprocessing pipeline
pipeline = Pipeline([
    TissueDetectionHE(),
    StainNormalizationHE(target="normalize", stain_estimation_method="macenko"),
])

# Run the pipeline on the slide (SlideData.run handles tiling + transforms)
wsi.run(pipeline)

# Access processed tiles
for tile in wsi.tiles:
    processed_image = tile.image
    tissue_mask = tile.masks["tissue"]

Common Workflows

H&E Image Analysis:

1. Load WSI with appropriate slide class
2. Apply tissue detection and stain normalization
3. Perform nucleus detection or train segmentation models
4. Extract features and build spatial graphs
5. Conduct downstream analysis

Multiparametric Imaging (CODEX):

1. Load CODEX slide with CODEXSlide
2. Collapse multi-run channel data with CollapseRunsCODEX
3. Segment cells using SegmentMIF (Mesmer)
4. Quantify per-cell marker expression with QuantifyMIF
5. Read the resulting AnnData from slide.counts for single-cell analysis

Training ML Models:

1. Prepare data with a pathml.datasets DataModule (e.g. PanNukeDataModule) or a TileDataset
2. Train HoVerNet (or another model) with a standard PyTorch loop
3. Post-process predictions with post_process_batch_hovernet
4. Evaluate on held-out test sets
5. Optionally export to ONNX for inference

Reference Files

Load the relevant reference for detailed API, workflows, and gotchas:

• references/image_loading.md - WSI formats, slide classes, loading strategies
• references/preprocessing.md - transform catalog, pipeline construction, stain normalization
• references/graphs.md - graph builders, feature extraction, spatial analysis
• references/machine_learning.md - HoVer-Net/HACTNet, training, datasets, ONNX inference
• references/multiparametric.md - CODEX/Vectra/multiplex IF, cell segmentation, quantification
• references/data_management.md - h5path storage, tile management, batch processing

PathML's API surface shifts between releases; treat the reference code as workflow scaffolding and confirm exact class/method names against the version you have installed (python -c "import pathml; print(pathml.__version__)") and the official API docs at https://pathml.readthedocs.io/.