Recently there have been many algorithms proposed for the classification of very high resolution whole slide images (WSIs). These new algorithms are mostly focused on finding novel ways to combine the information from small local patches extracted from the slide, with an emphasis on effectively aggregating more global information for the final predictor. In this paper we thoroughly explore different key design choices for WSI classification algorithms to investigate what matters most for achieving high accuracy. Surprisingly, we found that capturing global context information does not necessarily mean better performance. A model that captures the most global information consistently performs worse than a model that captures less global information. In addition, a very simple multi-instance learning method that captures no global information performs almost as well as models that capture a lot of global information. These results suggest that the most important features for effective WSI classification are captured at the local small patch level, where cell and tissue micro-environment detail is most pronounced. Another surprising finding was that unsupervised pre-training on a larger set of 33 cancers gives significantly worse performance compared to pre-training on a smaller dataset of 7 cancers (including the target cancer). We posit that pre-training on a smaller, more focused dataset allows the feature extractor to make better use of the limited feature space to better discriminate between subtle differences in the input patch.

翻译：近期，针对极高分辨率全切片图像（WSI）的分类算法层出不穷。这些新算法主要聚焦于探索从切片中提取的局部小块信息的新颖整合方式，着重于有效聚合更多全局信息以构建最终预测器。本文系统探究了WSI分类算法中不同关键设计选择，旨在揭示影响高精度性能的核心要素。令人意外的是，我们发现捕获全局上下文信息并不必然提升性能。一个捕获最多全局信息的模型，其表现始终逊色于捕获较少全局信息的模型。此外，一种完全不捕获全局信息的简单多实例学习方法，其性能几乎与大量捕获全局信息的模型相当。这些结果表明，有效WSI分类的最关键特征源自局部小块层级的细节——即细胞与组织微环境特征最显著的层面。另一项意外发现是：在包含33种癌种的大型数据集上进行无监督预训练，其性能显著低于在包含7种癌种（含目标癌种）的小型数据集上的预训练。我们推测，在规模更小、更聚焦的数据集上进行预训练，能使特征提取器更高效地利用有限特征空间，更好地区分输入块中的细微差异。