pathml VS slideflow

We have been using dask to support our computational pathology workflows [1], where the images are so big that they cannot be loaded in memory, let alone analyzed (standard pathology whole slide images are ~1GB; some microscopy techniques generate images >1TB). We divide each image into a bunch of smaller tiles and process each tile independently. The dask.distributed scheduler lets us scale up by distributing the tile processing across a cluster.

Benefits of dask.distributed: easy to get up and running, and has support for spinning up clusters on lots of different computing platforms (local machines, HPC cluster, k8s, etc.)

One difficulty is optimizing performance - there are so many configuration details (job size, number of workers, worker resources, etc. etc.) that it's been hard to know what is best.

[1] https://github.com/Dana-Farber-AIOS/pathml

Easy-to-use API with clear documentation

I run an open source, medical AI project for digital pathology called Slideflow at the University of Chicago. We’re working on developing reliable biomarkers for patients with lung, breast, and thyroid cancer, and we can always use more help! Got lots of interesting active projects - uncertainty quantification, generative models, embedded systems deployment - send me a DM if it sounds interesting!

After years of development, we've released our open-source deep learning framework for digital histology, Slideflow (https://github.com/jamesdolezal/slideflow). It has flexible and highly optimized whole-slide image processing, support for a wide variety of existing and custom architectures (with continuous, categorical, or time-series outcomes), real-time digital stain normalization, a number of explainability tools, and integrated uncertainty quantification. It's compatible with both Tensorflow and PyTorch, available on PyPI and DockerHub, and comes with good documentation (https://slideflow.dev/). We've tried out a number of alternative frameworks over the years, and I think the easy of use, flexibility, and performance optimizations set it apart from other repos you'll find on GitHub.