# EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision

arXiv: https://arxiv.org/abs/2507.06639 · HTML: https://mlsanity.com/paper/2507.06639/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Multimodal Whole Slide Foundation Model for Pathology](https://mlsanity.com/paper/2411.19666/) (2024)
- [PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology](https://mlsanity.com/paper/2405.10254/) (2024)
- [Solving Inefficiency of Self-supervised Representation Learning](https://mlsanity.com/paper/2104.08760/) (2021)
- [Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods](https://mlsanity.com/paper/2209.15589/) (2022)
- [EXAONEPath 1.0 Patch-level Foundation Model for Pathology](https://mlsanity.com/paper/2408.00380/) (2024)

## Built on by (most cited first, 1 of 1)

- [EXAONE Path 2.5: Pathology Foundation Model with Multi-Omics Alignment](https://mlsanity.com/paper/2512.14019/) (2025)

## Abstract (arXiv)

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.