在多中心皮肤癌数据集上评估病理基础模型的泛化能力
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping
- 用多中心数据构建MIL框架，测试基础模型的切片级特征提取能力
- 发现减少偏差特征可显著提升相似性分类器的性能
- 提出新指标FM-SI，量化模型对分布偏移的鲁棒性
在大规模、域内数据上预训练使病理基础模型（FM）能够学习通用数据表征，提升下游任务的迁移性能。在计算病理学中，全幻灯片图像分析因图像规模达百万像素级，需采用多实例学习（MIL）框架。不同病理基础模型间的差异凸显了设计真实世界挑战以评估其有效性的必要性。为此，本文提出一种新基准，评估病理基础模型作为切片级特征提取器在MIL分类框架中的表现。我们利用AI4SkIN数据集——一个包含具有挑战性的皮肤梭形细胞肿瘤亚型的多中心队列。同时提出基础模型-轮廓指数（FM-SI），用于衡量模型在分布偏移下的一致性。实验表明，提取更少偏差的特征能显著提升分类性能，尤其在基于相似性的MIL分类器中。
原文摘要 · Abstract (English)
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.
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