Du Xianglong, Guo JiaQi, Xing Zehang, Liu Miao, Xu Zhengyang, Ruan Chenglin, Wen Yuting, Wang Yi, Cui Lei, Li Hansheng
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782609.
Multiple instance learning(MIL) has shown superior performance in the classification of whole-slide images(WSIs). The implementation of multiple instance learning for WSI classification typically involves two components, i.e., a feature extractor, which is used to extract features from patches, and an MIL aggregator, responsible for generating WSI features from the patch features, also contributing to the final classification of WSIs. MIL aggregators often employs a specific MIL classification module. To ensure interactive optimization of the feature extractor and the MIL aggregator, existing state-of-the-art methods select patches based on attention scores to optimize the feature extractor. However, they predominantly focus on easy-to-classify instances, leading to inadequate capabilities in discriminating hard-toclassify instances. In this paper, we introduces a novel Multiple Instance Learning method, HPA-MIL (Hard Pseudo-label Assignment), which directly mines hard instances through pseudo-label assignment. Our experiments demonstrate that HPA-MIL achieves an AUC of 0.9523 on the TCGA NSCLC dataset, which outperforms all the existing state-of-the-art methods compared.
多实例学习（MIL）在全切片图像（WSI）分类中表现出卓越性能。用于WSI分类的多实例学习实现通常涉及两个组件，即用于从图像块中提取特征的特征提取器，以及负责从图像块特征生成WSI特征并对WSI最终分类做出贡献的MIL聚合器。MIL聚合器通常采用特定的MIL分类模块。为确保特征提取器和MIL聚合器的交互式优化，现有最先进方法基于注意力分数选择图像块来优化特征提取器。然而，它们主要关注易于分类的实例，导致在区分难以分类的实例方面能力不足。在本文中，我们介绍了一种新颖的多实例学习方法，即硬伪标签分配多实例学习（HPA-MIL），它通过伪标签分配直接挖掘硬实例。我们的实验表明，HPA-MIL在TCGA NSCLC数据集上实现了0.9523的AUC，优于所有与之比较的现有最先进方法。
