Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
Foundation models in digital pathology use massive datasets to learn useful
compact feature representations of complex histology images. However, there is
limited transparency into what drives the correlation between dataset size and
performance, raising the question of whether simply adding more data to
increase performance is always necessary. In this study, we propose a
prototype-guided diffusion model to generate high-fidelity synthetic pathology
data at scale, enabling large-scale self-supervised learning and reducing
reliance on real patient samples while preserving downstream performance. Using
guidance from histological prototypes during sampling, our approach ensures
biologically and diagnostically meaningful variations in the generated data. We
demonstrate that self-supervised features trained on our synthetic dataset
achieve competitive performance despite using ~60x-760x less data than models
trained on large real-world datasets. Notably, models trained using our
synthetic data showed statistically comparable or better performance across
multiple evaluation metrics and tasks, even when compared to models trained on
orders of magnitude larger datasets. Our hybrid approach, combining synthetic
and real data, further enhanced performance, achieving top results in several
evaluations. These findings underscore the potential of generative AI to create
compelling training data for digital pathology, significantly reducing the
reliance on extensive clinical datasets and highlighting the efficiency of our
approach.
