For the past 5 years, our CLAM toolbox has been widely used by the community for whole-slide image (WSI) processing. For scalability, today we are excited to announce the release of two major internally developed open libraries for large scale batch processing of pathology WSIs and foundation model benchmarking: Trident (https://t.co/J2dfveHYjC) and Patho-Bench (https://t.co/DfeFTwCZPM).
Read the excellent blog from our superstar grad student Andrew Zhang @aspartate_ai explaining why we built these tools and how you can benefit and contribute: https://t.co/6MEqggo6nf
Trident includes,
- Computationally efficient tools for handling very large scale batch processing of WSI maximizing GPU and CPU compute.
- Support for all SOTA patch-level and slide level foundation models, including latest foundation models from our group UNI 2 (https://t.co/5Gkyzd8R8a) and TITAN (https://t.co/MyoVWlRu1a).
Patho-Bench includes,
- A large benchmark to standardize evaluation of pathology FMs curated from publicly available data, featuring 42 clinically relevant tasks (including disease subtyping, grading, treatment response, survival/outcome prediction, IHC scoring, biomarker and molecular alteration prediction).
- Experiment handlers for running all combination of modeling choices and tasks including linear probing, survival, etc.
- The ability to run hundreds of experiments using a single command, with automatic GPU load-balancing, experiment monitoring, and results collection.
Preprint: https://t.co/0OQzzPD7rC
Congrats to @aspartate_ai, @GuillaumeJaume , @anurag_vaidya7, and the rest of the team! Stay tuned for more to come soon.
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