pathml

Computational pathology ML pipelines unify loading, preprocessing, and model training for whole-slide images. PathML provides an integrated toolkit to process diverse pathology datasets and apply scalable workflows for segmentation, graph construction, and spatial analysis.

Computational pathology preprocessing supports over 160 slide formats, including standard whole-slide images and multiplex-imaging modalities like CODEX and Vectra. This broad compatibility ensures diverse pathology datasets can be loaded and processed within a single extensible toolkit.

Pathology image segmentation workflows are fully compatible with standard Python ML libraries. The toolkit ensures modularity and extensibility, allowing researchers to integrate loading, preprocessing, and tissue graph construction directly into existing Python-based machine learning pipelines.

Yes, this computational pathology toolkit explicitly supports multiplex imaging, including CODEX and Vectra formats. It enables researchers to load and preprocess these complex multiplex datasets for downstream spatial analysis and machine learning model training.

Constructing tissue graphs from whole-slide images involves loading the slide, segmenting nuclei, and building spatial relationships. PathML integrates these steps into a modular pipeline, enabling scalable graph construction for biomedical imaging research.

Processing multi-format pathology data requires modular pipelines to handle diverse modalities like CODEX and standard WSI. While the toolkit ensures compatibility with Python ML libraries, scalable training depends on properly structuring spatial analysis and segmentation workflows for reproducible experimentation.