自监督增强数字病理学中基于实例的多示例学习方法：一项基准研究
Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study
AI总结：
本研究在4个数据集上进行710次实验，发现借助优质自监督特征提取器，简单的基于实例的MIL方法能达到或超越复杂的基于嵌入的SOTA方法，且更具可解释性，建议将研究重心转向适配WSI的SSL方法。
摘要：
多示例学习（MIL）已成为全切片图像（WSI）分类的最佳解决方案。它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示例包。MIL包括两种主要方法：基于实例的方法和基于嵌入的方法。在基于实例的方法中，每个图像块被独立分类，然后将图像块的分数聚合以预测包标签。在基于嵌入的方法中，在聚合图像块嵌入之后进行包分类。尽管基于实例的方法天然更具可解释性，但由于对较差的特征提取器具有鲁棒性，过去通常更倾向于使用基于嵌入的MIL。然而，最近通过自监督学习（SSL），特征嵌入的质量有了大幅提升。尽管如此，许多作者仍继续支持基于嵌入的MIL的优越性。为了进一步调查这一点，我们在4个数据集上进行了710次实验，比较了10种MIL策略、6种具有4种骨干网络的自监督方法、4种基础模型以及各种适应病理学的技术。此外，我们引入了4种以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，借助良好的SSL特征提取器，参数极少的简单基于实例的MIL能够获得与复杂的、最先进的（SOTA）基于嵌入的MIL方法相似或更好的性能，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说明性，我们的结果表明，应该将更多精力投入到适应WSI的SSL方法中，而不是复杂的基于嵌入的MIL方法中。
英文摘要：
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.
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