EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911105073283072 |
|---|---|
| author | Pyeon, Myeongjang Lee, Janghyeon Lee, Minsoo Yun, Juseung Choi, Hwanil Kim, Jonghyun Kim, Jiwon Hu, Yi Jang, Jongseong Lee, Soonyoung |
| author_facet | Pyeon, Myeongjang Lee, Janghyeon Lee, Minsoo Yun, Juseung Choi, Hwanil Kim, Jonghyun Kim, Jiwon Hu, Yi Jang, Jongseong Lee, Soonyoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06639 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision Pyeon, Myeongjang Lee, Janghyeon Lee, Minsoo Yun, Juseung Choi, Hwanil Kim, Jonghyun Kim, Jiwon Hu, Yi Jang, Jongseong Lee, Soonyoung Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.06639 |