pathml

Whole-slide pathology workflows can be automated using a modular framework that orchestrates preprocessing, nucleus detection, and graph analysis. This enables reproducible end-to-end pipelines across diverse pathology datasets without manual stitching of individual steps.

Reproducible machine learning pipelines for multiparametric imaging are built by defining modular preprocessing and model deployment steps within a unified framework. This allows researchers to construct, validate, and extend data-driven pathology workflows efficiently.

PathML supports graph analysis and nucleus detection for clinical research pipelines by providing built-in references for image loading and ML workflows. It applies to both research and clinical contexts for analyzing whole-slide pathology data.

You can get started by loading a sample slide dataset and running the provided quick-start pipeline. This generates annotated results rapidly, allowing you to prototype and extend your pathology workflows without building from scratch.

No specific external dependencies are required to run the core pathology toolkit. It includes built-in references for image loading, machine learning, graphs, and data management to accelerate development out of the box.

A modular pathology workflow solves the problem of stitching together standalone preprocessing, segmentation, and ML scripts. It provides a unified framework to define, reproduce, and extend end-to-end analysis pipelines across diverse datasets.