ahcore 0.1 - first version
First version of ahcore
Ahcore is the AI for Oncology public toolkit for computational pathology. It's goal is to eventually support all computational pathology workflows, such as segmentation, detection but also support advanced self-supervised pipelines and foundational models.
Features
Ahcore is a computational pathology toolkit, in the first public release we only support segmentation.
- Lightning AI-based computational pathology pipeline
- MONAI model support
- GPU-based augmentation pipeline based on Kornia
- Data loading supported by dlup
- Callbacks supporting the tile-by-tile inference and writing to TIFF
- Callbacks supporting to compute the whole-slide level metrics
- Hydra-based configuration pipeline.
Note: Ahcore v0.1 currently only supports segmentation, but detection will be added in the next version.
Documentation
A bit more documentation is available at https://docs.aiforoncology.nl/ahcore, and will be extended in the coming period, also including a few trained models.
Credits
Many members of the AI for Oncology lab were involved in preparing this version. Special thanks to (in no particular order):
- Eric Marcus @EricMarcus-ai
- Jonas Teuwen @jonasteuwen
- Vanessa Botha @VanessaBotha
- Marek Oerlemans @moerlemans
- Ajey Pai @AjeyPaiK
And the NKI's computational pathology lab:
- Hugo Horlings
- Bart de Rooij @BPdeRooij
Third-parties
Many thanks to all the authors and contributors of our dependencies, and the author of the wonderful lightning-hydra-template.
