Abstract
Pretraining on large-scale, in-domain datasets grants histopathology foundation models (FM) the ability to learn task-agnostic data representations, enhancing transfer learning on downstream tasks. In computational pathology, automated whole slide image analysis requires multiple instance learning (MIL) frameworks due to the gigapixel scale of the slides. The diversity among histopathology FMs has highlighted the need to design real-world challenges for evaluating their effectiveness. To bridge this gap, our work presents a novel benchmark for evaluating histopathology FMs as patch-level feature extractors within a MIL classification framework. For that purpose, we leverage the AI4SkIN dataset, a multi-center cohort encompassing slides with challenging cutaneous spindle cell neoplasm subtypes. We also define the Foundation Model - Silhouette Index (FM-SI), a novel metric to measure model consistency against distribution shifts. Our experimentation shows that extracting less biased features enhances classification performance, especially in similarity-based MIL classifiers.
Abstract (translated)
大规模、领域内数据集的预训练赋予了组织病理学基础模型（Foundation Models，简称 FM）学习任务无关的数据表示能力，从而增强了其在下游任务中的迁移学习效果。在计算病理学中，由于全片扫描图像达到数十亿像素的规模，自动化分析需要采用多实例学习（Multiple Instance Learning, MIL）框架。组织病理学基础模型之间的多样性凸显了设计现实世界挑战以评估它们有效性的必要性。 为弥合这一差距，我们的工作提出了一项新的基准测试，用于在基于MIL分类框架中作为补丁级特征提取器的组织病理学基础模型的效果进行评价。为此，我们利用了AI4SkIN数据集，该数据集中涵盖了多中心队列的皮肤梭形细胞肿瘤亚型全片扫描图像，这些图像极具挑战性。 此外，我们还定义了一个新的评估标准——Foundation Model - Silhouette Index（FM-SI），用于衡量模型在面对分布偏移时的一致性。实验结果表明，在基于相似性的MIL分类器中，提取出较少偏置特征能显著提升分类性能。
URL
https://arxiv.org/abs/2506.18668
