Multiple Instance Learning (MIL) has emerged as the best solution for Whole Slide Image (WSI) classification. It consists of dividing each slide into patches, which are treated as a bag of instances labeled with a global label. MIL includes two main approaches: instance-based and embedding-based. In the former, each patch is classified independently, and then the patch scores are aggregated to predict the bag label. In the latter, bag classification is performed after aggregating patch embeddings. Even if instance-based methods are naturally more interpretable, embedding-based MILs have usually been preferred in the past due to their robustness to poor feature extractors. However, recently, the quality of feature embeddings has drastically increased using self-supervised learning (SSL). Nevertheless, many authors continue to endorse the superiority of embedding-based MIL. To investigate this further, we conduct 710 experiments across 4 datasets, comparing 10 MIL strategies, 6 self-supervised methods with 4 backbones, 4 foundation models, and various pathology-adapted techniques. Furthermore, we introduce 4 instance-based MIL methods never used before in the pathology domain. Through these extensive experiments, we show that with a good SSL feature extractor, simple instance-based MILs, with very few parameters, obtain similar or better performance than complex, state-of-the-art (SOTA) embedding-based MIL methods, setting new SOTA results on the BRACS and Camelyon16 datasets. Since simple instance-based MIL methods are naturally more interpretable and explainable to clinicians, our results suggest that more effort should be put into well-adapted SSL methods for WSI rather than into complex embedding-based MIL methods.

翻译：多示例学习（MIL）已成为全切片图像（WSI）分类的最佳解决方案。其方法是将每张切片划分为多个图像块，这些图像块被视为一个带有全局标签的实例包。MIL主要包括两种方法：基于实例的方法和基于嵌入的方法。在前者中，每个图像块被独立分类，然后汇总图像块得分以预测包标签。在后者中，包分类是在汇总图像块嵌入之后进行的。尽管基于实例的方法天然更具可解释性，但过去通常更青睐基于嵌入的MIL方法，因为它们对低质量特征提取器具有更强的鲁棒性。然而，最近通过自监督学习（SSL）的使用，特征嵌入的质量得到了显著提升。尽管如此，许多研究者仍继续支持基于嵌入的MIL方法的优越性。为了进一步探究此问题，我们在4个数据集上进行了710项实验，比较了10种MIL策略、6种自监督方法（搭配4种骨干网络）、4个基础模型以及多种病理学适配技术。此外，我们引入了4种此前从未在病理学领域使用过的基于实例的MIL方法。通过这些广泛的实验，我们表明，在使用良好的SSL特征提取器时，参数极少的简单基于实例的MIL方法，能够获得与复杂的、最先进的（SOTA）基于嵌入的MIL方法相似甚至更优的性能，并在BRACS和Camelyon16数据集上创造了新的SOTA结果。由于简单的基于实例的MIL方法天然对临床医生更具可解释性和可理解性，我们的结果表明，应将更多精力投入到为WSI量身定制的SSL方法上，而非复杂的基于嵌入的MIL方法。