计算病理学中嵌入聚合方法的基准测试：临床数据视角
Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective
AI总结：
本研究对十种切片级嵌入聚合方法在九个临床任务上进行基准测试，发现领域特定基础模型优于通用模型，空间感知聚合器仅在ImageNet预训练模型下有效，且无单一模型普遍最优，强调需开发更通用的聚合技术。
摘要：
人工智能（AI）的最新进展，特别是基础模型（FMs）的自监督学习，正在彻底改变医学影像和计算病理学（CPath）。在分析数字全切片图像（WSIs）时，一个持续的挑战是如何将数万个图块级别的图像嵌入聚合为切片级别的表示。由于方法开发中普遍使用为基因组研究创建的数据集（如TCGA），这些技术在临床实践中的诊断切片上的性能尚未得到充分探索。本研究对十种切片级聚合技术进行了全面的基准分析，涵盖九个临床相关任务，包括诊断评估、生物标志物分类和结果预测。结果产生了以下关键见解：（1）来自领域特定（组织学图像）FMs的嵌入在聚合方法上优于基于通用ImageNet模型的嵌入。（2）空间感知聚合器在使用ImageNet预训练模型时显著提升性能，但在使用FMs时则不然。（3）没有单一模型在所有任务中表现出色，空间感知模型也未显示出预期的普遍优越性。这些发现强调了对更具适应性和普遍适用性的聚合技术的需求，为未来研究指引方向，以开发更好地满足病理学中临床AI不断演变需求的工具。本工作使用的代码可在\url{https://github.com/fuchs-lab-public/CPath_SABenchmark}获取。
英文摘要：
Recent advances in artificial intelligence (AI), in particular self-supervised learning of foundation models (FMs), are revolutionizing medical imaging and computational pathology (CPath). A constant challenge in the analysis of digital Whole Slide Images (WSIs) is the problem of aggregating tens of thousands of tile-level image embeddings to a slide-level representation. Due to the prevalent use of datasets created for genomic research, such as TCGA, for method development, the performance of these techniques on diagnostic slides from clinical practice has been inadequately explored. This study conducts a thorough benchmarking analysis of ten slide-level aggregation techniques across nine clinically relevant tasks, including diagnostic assessment, biomarker classification, and outcome prediction. The results yield following key insights: (1) Embeddings derived from domain-specific (histological images) FMs outperform those from generic ImageNet-based models across aggregation methods. (2) Spatial-aware aggregators enhance the performance significantly when using ImageNet pre-trained models but not when using FMs. (3) No single model excels in all tasks and spatially-aware models do not show general superiority as it would be expected. These findings underscore the need for more adaptable and universally applicable aggregation techniques, guiding future research towards tools that better meet the evolving needs of clinical-AI in pathology. The code used in this work is available at \url{https://github.com/fuchs-lab-public/CPath_SABenchmark}.
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。
