EXAONE Path 2.5: Pathology Foundation Model with Multi-Omics Alignment

TL;DR

EXAONE Path 2.5 introduces a multimodal pathology foundation model that integrates histologic, genomic, epigenetic, and transcriptomic data to better reflect tumor biology and improve precision oncology.

Contribution

The paper presents a novel multimodal foundation model with specialized modules and contrastive learning for comprehensive tumor representation, outperforming existing models.

Findings

Achieves state-of-the-art performance on Patho-Bench tasks.

Demonstrates high data and parameter efficiency.

Shows superior adaptability in clinical settings.

Abstract

Cancer progression arises from interactions across multiple biological layers, especially beyond morphological and across molecular layers that remain invisible to image-only models. To capture this broader biological landscape, we present EXAONE Path 2.5, a pathology foundation model that jointly models histologic, genomic, epigenetic and transcriptomic modalities, producing an integrated patient representation that reflects tumor biology more comprehensively. Our approach incorporates three key components: (1) multimodal SigLIP loss enabling all-pairwise contrastive learning across heterogeneous modalities, (2) a fragment-aware rotary positional encoding (F-RoPE) module that preserves spatial structure and tissue-fragment topology in WSI, and (3) domain-specialized internal foundation models for both WSI and RNA-seq to provide biologically grounded embeddings for robust multimodal alignment. We evaluate EXAONE Path 2.5 against six leading pathology foundation models across two complementary benchmarks: an internal real-world clinical dataset and the Patho-Bench benchmark covering 80 tasks. Our framework demonstrates high data and parameter efficiency, achieving on-par performance with state-of-the-art foundation models on Patho-Bench while exhibiting the highest adaptability in the internal clinical setting. These results highlight the value of biologically informed multimodal design and underscore the potential of integrated genotype-to-phenotype modeling for next-generation precision oncology.