Welcome to PathML’s documentation!

PathML is a Python package for computational pathology.

PathML is a toolbox to facilitate machine learning workflows for high-resolution whole-slide pathology images. This includes modular pipelines for preprocessing, PyTorch DataLoaders for training and benchmarking machine learning model performance on standardized datasets, support for sharing preprocessing pipelines, pretrained models, and more.

Development is a collaboration between the AI Operations and Data Science Group in the Department of Informatics and Analytics at Dana-Farber Cancer Institute and the Department of Pathology and Laboratory Medicine at Weill Cornell Medicine.

Getting Started

• Overview
  • License
• Installation

Preprocessing

• Loading Images
  • Individual Images
  • Datasets of Images
  • Supported slide types
  • Supported file formats
• Creating Preprocessing Pipelines
  • What is a Transform?
  • What is a Pipeline?
  • Creating custom Transforms
• Running Preprocessing Pipelines
  • How it works
  • Preprocessing a single WSI
  • Preprocessing a dataset of WSI
  • Distributed processing
• HDF5 Integration
  • Overview
  • How it Works
  • About HDF5
  • .h5path File Format
  • Reading and Writing

Datasets

• Datasets
  • DataModules
  • Using public datasets
  • References

Machine Learning

• DataLoaders
• Models
  • References

Examples

• Loading Images: Quickstart
  • Aperio SVS
  • Generic tiled TIFF
  • Hamamatsu NDPI
  • Hamamatsu VMS
  • Leica SCN
  • MIRAX
  • Olympus VSI
  • Trestle TIFF
  • Ventana BIF
  • Zeiss ZVI
  • DICOM
  • Volumetric + time-series OME-TIFF
  • CODEX spatial proteomics
  • MERFISH spatial gene expression
  • Visium 10x spatial gene expression
• H&E Stain Deconvolution and Color Normalization
  • References
• Brightfield Imaging: Quickstart
• Multiparametric Imaging: Quickstart
  • Defining a Multiparametric Pipeline
  • AnnData Integration and Spatial Single Cell Analysis
  • References
• Multiparametric Imaging: CODEX
  • Reading the slides
  • Define and run the preprocessing pipeline
  • Extract and concatenate the resulting count matrices
  • Annotate the clusters based on the markers intensity
  • Identification of cellular neighborhoods
• Training an ML Model (HoVer-Net)
  • Data augmentation
  • Load PanNuke dataset
  • Model Training
  • Evaluate Model
  • Examples
  • Conclusion
  • References
  • Session info
• Preprocessing Transforms Gallery
  • Transforms that modify an image
  • Transforms that create a mask
  • Transforms that modify a mask

API Reference

• Core API
  • SlideData
  • Slide Types
  • Tile
  • SlideDataset
  • Tiles and Masks helper classes
  • Slide Backends
  • h5pathManager
• Preprocessing API
  • Pipeline
  • Transforms
• Datasets API
  • PanNuke
  • DeepFocus
• ML API
  • h5path Dataset
  • HoVer-Net
• Utilities API
  • Logging Utils
  • Core Utils
  • Datasets Utils
  • ML Utils
  • Miscellaneous Utils

Contributing

• Contributing
  • Submitting a bug report
  • Requesting a new feature
  • For developers
  • Thank You!

Indices and tables

• Index

• Search Page