  Pathology is the study of microscopic inspection of tissue, and a pathology
diagnosis is often the medical gold standard to diagnose disease. Pathology
images provide a unique challenge for computer-vision-based analysis: a single
pathology Whole Slide Image (WSI) is gigapixel-sized and often contains
hundreds of thousands to millions of objects of interest across multiple
resolutions. In this work, we propose PathoLogy Universal TransfOrmer (PLUTO):
a light-weight pathology FM that is pre-trained on a diverse dataset of 195
million image tiles collected from multiple sites and extracts meaningful
representations across multiple WSI scales that enable a large variety of
downstream pathology tasks. In particular, we design task-specific adaptation
heads that utilize PLUTO's output embeddings for tasks which span pathology
scales ranging from subcellular to slide-scale, including instance
segmentation, tile classification, and slide-level prediction. We compare
PLUTO's performance to other state-of-the-art methods on a diverse set of
external and internal benchmarks covering multiple biologically relevant tasks,
tissue types, resolutions, stains, and scanners. We find that PLUTO matches or
outperforms existing task-specific baselines and pathology-specific foundation
models, some of which use orders-of-magnitude larger datasets and model sizes
when compared to PLUTO. Our findings present a path towards a universal
embedding to power pathology image analysis, and motivate further exploration
around pathology foundation models in terms of data diversity, architectural
improvements, sample efficiency, and practical deployability in real-world
applications.
