Digital pathology has significantly advanced disease detection and
pathologist efficiency through the analysis of gigapixel whole-slide images
(WSI). In this process, WSIs are first divided into patches, for which a
feature extractor model is applied to obtain feature vectors, which are
subsequently processed by an aggregation model to predict the respective WSI
label. With the rapid evolution of representation learning, numerous new
feature extractor models, often termed foundational models, have emerged.
Traditional evaluation methods, however, rely on fixed aggregation model
hyperparameters, a framework we identify as potentially biasing the results.
Our study uncovers a co-dependence between feature extractor models and
aggregation model hyperparameters, indicating that performance comparability
can be skewed based on the chosen hyperparameters. By accounting for this
co-dependency, we find that the performance of many current feature extractor
models is notably similar. We support this insight by evaluating seven feature
extractor models across three different datasets with 162 different aggregation
model configurations. This comprehensive approach provides a more nuanced
understanding of the relationship between feature extractors and aggregation
models, leading to a fairer and more accurate assessment of feature extractor
models in digital pathology.

数字病理学通过对 G 级全幅图像（WSI）的分析大大提高了疾病检测和病理学家的效率。我们的研究揭示了特征提取模型和聚合模型超参数之间的相互依赖性，指出选择的超参数可能会使性能可比性产生偏差。通过对 162 个不同聚合模型配置的三个不同数据集上的七个特征提取模型的评估，我们提供了关于特征提取器和聚合模型之间关系的更细致的理解，从而使数字病理学中的特征提取模型能够获得更公正和准确的评估。