EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
In digital pathology, whole-slide images (WSIs) are often difficult to handle
due to their gigapixel scale, so most approaches train patch encoders via
self-supervised learning (SSL) and then aggregate the patch-level embeddings
via multiple instance learning (MIL) or slide encoders for downstream tasks.
However, patch-level SSL may overlook complex domain-specific features that are
essential for biomarker prediction, such as mutation status and molecular
characteristics, as SSL methods rely only on basic augmentations selected for
natural image domains on small patch-level area. Moreover, SSL methods remain
less data efficient than fully supervised approaches, requiring extensive
computational resources and datasets to achieve competitive performance. To
address these limitations, we present EXAONE Path 2.0, a pathology foundation
model that learns patch-level representations under direct slide-level
supervision. Using only 37k WSIs for training, EXAONE Path 2.0 achieves
state-of-the-art average performance across 10 biomarker prediction tasks,
demonstrating remarkable data efficiency.
