EXAONE Path 2.0

Introduction

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. For further details, please refer to EXAONE_Path_2_0_technical_report.pdf.

Quickstart

Load EXAONE Path 2.0 and extract features.

1. Prerequisites

NVIDIA GPU with 12GB+ VRAM
Python 3.12+

Note: This implementation requires NVIDIA GPU and drivers. The provided environment setup specifically uses CUDA-enabled PyTorch, making NVIDIA GPU mandatory for running the model.

2. Setup Python environment

git clone https://huggingface.co/LGAI-EXAONE/EXAONE-Path-2.0
cd EXAONE-Path-2.0
pip install -r requirements.txt

3. Load the model & Inference

from exaonepath import EXAONEPathV20

hf_token = "YOUR_HUGGING_FACE_ACCESS_TOKEN"
model = EXAONEPathV20.from_pretrained("LGAI-EXAONE/EXAONE-Path-2.0", use_auth_token=hf_token)

svs_path = "samples/sample.svs"
patch_features = model(svs_path)[0]

Model Performance Comparison

Performance of EXAONE Path 2.0 on 10 slide-level benchmarks (AUROC scores):

Benchmarks	TITAN	PRISM	CHIEF	Prov-GigaPath	UNI2-h	EXAONE Path 1.0	EXAONE Path 2.0
LUAD-TMB-USA1	0.690	0.645	0.650	0.674	0.669	0.692	0.664
LUAD-EGFR-USA1	0.754	0.815	0.784	0.709	0.827	0.784	0.853
LUAD-KRAS-USA2	0.541	0.623	0.468	0.511	0.469	0.527	0.645
CRC-MSI-KOR	0.937	0.943	0.927	0.954	0.981	0.972	0.938
BRCA-TP53-CPTAC	0.788	0.842	0.788	0.739	0.808	0.766	0.757
BRCA-PIK3CA-CPTAC	0.758	0.893	0.702	0.735	0.857	0.735	0.804
RCC-PBRM1-CPTAC	0.638	0.557	0.513	0.527	0.501	0.526	0.583
RCC-BAP1-CPTAC	0.719	0.769	0.731	0.697	0.716	0.719	0.807
COAD-KRAS-CPTAC	0.764	0.744	0.699	0.815	0.943	0.767	0.912
COAD-TP53-CPTAC	0.889	0.816	0.701	0.712	0.783	0.819	0.875
Average	0.748	0.765	0.696	0.707	0.755	0.731	0.784

License

The model is licensed under EXAONEPath AI Model License Agreement 1.0 - NC

Contact

LG AI Research Technical Support: [email protected]