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.
- Loading Images
- Creating Preprocessing Pipelines
- Running Preprocessing Pipelines
- HDF5 Integration
- Loading Images: Quickstart
- H&E Stain Deconvolution and Color Normalization
- Brightfield Imaging: Quickstart
- Multiparametric Imaging: Quickstart
- Multiparametric Imaging: CODEX
- Training an ML Model (HoVer-Net)
- Preprocessing Transforms Gallery