我们能否简化病理学基础模型的切片级微调？
Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?
AI总结：
本研究提出SiMLP，一种结合均值池化和多层感知器的简单非线性映射策略，无需复杂MIL即可高效微调病理基础模型至切片级任务，在泛癌分类等任务上超越现有方法，挑战传统微调范式。
摘要：
计算病理学中基础模型的出现已经改变了组织病理学图像分析，其中全切片成像（WSI）诊断是一个核心应用。传统上，通过多实例学习（MIL）进行的弱监督微调一直是使基础模型适应WSI的主要方法。然而，在这项工作中，我们提出了一个关键的实验发现：一种结合均值池化和多层感知器的简单非线性映射策略，称为SiMLP，可以有效地将补丁级基础模型适应到切片级任务，而无需复杂的基于MIL的学习。通过在多种下游任务上的广泛实验，我们展示了SiMLP相对于最先进方法的优越性能。例如，在一个大规模泛癌分类任务中，SiMLP超过了流行的基于MIL的方法3.52%。此外，SiMLP在少样本分类中表现出强大的学习能力，并且与在数万张切片上预训练的切片级基础模型保持高度竞争力。最后，SiMLP在肺癌亚型分类中表现出显著的鲁棒性和可迁移性。总的来说，我们的发现挑战了传统的基于MIL的微调范式，表明仅凭一种任务无关的表示策略就能有效地将基础模型适应到WSI分析。这些见解为数字病理学的未来研究提供了独特而有意义的视角，为更高效和广泛适用的方法论铺平了道路。
英文摘要：
The emergence of foundation models in computational pathology has transformed histopathological image analysis, with whole slide imaging (WSI) diagnosis being a core application. Traditionally, weakly supervised fine-tuning via multiple instance learning (MIL) has been the primary method for adapting foundation models to WSIs. However, in this work we present a key experimental finding: a simple nonlinear mapping strategy combining mean pooling and a multilayer perceptron, called SiMLP, can effectively adapt patch-level foundation models to slide-level tasks without complex MIL-based learning. Through extensive experiments across diverse downstream tasks, we demonstrate the superior performance of SiMLP with state-of-the-art methods. For instance, on a large-scale pan-cancer classification task, SiMLP surpasses popular MIL-based methods by 3.52%. Furthermore, SiMLP shows strong learning ability in few-shot classification and remaining highly competitive with slide-level foundation models pretrained on tens of thousands of slides. Finally, SiMLP exhibits remarkable robustness and transferability in lung cancer subtyping. Overall, our findings challenge the conventional MIL-based fine-tuning paradigm, demonstrating that a task-agnostic representation strategy alone can effectively adapt foundation models to WSI analysis. These insights offer a unique and meaningful perspective for future research in digital pathology, paving the way for more efficient and broadly applicable methodologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
