Given the special situation of modeling gigapixel images, multiple instance learning (MIL) has become one of the most important frameworks for Whole Slide Image (WSI) classification. In current practice, most MIL networks often face two unavoidable problems in training: i) insufficient WSI data and ii) the sample memorization inclination inherent in neural networks. These problems may hinder MIL models from adequate and efficient training, suppressing the continuous performance promotion of classification models on WSIs. Inspired by the basic idea of Mixup, this paper proposes a new Pseudo-bag Mixup (PseMix) data augmentation scheme to improve the training of MIL models. This scheme generalizes the Mixup strategy for general images to special WSIs via pseudo-bags so as to be applied in MIL-based WSI classification. Cooperated by pseudo-bags, our PseMix fulfills the critical size alignment and semantic alignment in Mixup strategy. Moreover, it is designed as an efficient and decoupled method, neither involving time-consuming operations nor relying on MIL model predictions. Comparative experiments and ablation studies are specially designed to evaluate the effectiveness and advantages of our PseMix. Experimental results show that PseMix could often assist state-of-the-art MIL networks to refresh their classification performance on WSIs. Besides, it could also boost the generalization performance of MIL models in special test scenarios, and promote their robustness to patch occlusion and label noise. Our source code is available at https://github.com/liupei101/PseMix.

翻译：针对千兆像素图像建模的特殊场景，多示例学习（MIL）已成为全切片图像（WSI）分类最重要的框架之一。在实际应用中，大多数MIL网络在训练过程中常面临两个难以避免的问题：i) WSI数据不足，ii) 神经网络固有的样本记忆倾向。这些问题可能阻碍MIL模型进行充分有效的训练，从而抑制WSI分类模型性能的持续提升。受Mixup基本思想的启发，本文提出一种新的伪包混叠（PseMix）数据增强方案，用于改进MIL模型的训练。该方案通过伪包将通用图像的Mixup策略推广至特殊WSI场景，从而应用于基于MIL的WSI分类。借助伪包的协同作用，PseMix实现了Mixup策略中关键的尺寸对齐与语义对齐。此外，该方法被设计为一种高效且解耦的算法，既不涉及耗时操作，也不依赖MIL模型的预测结果。我们专门设计了对比实验和消融研究来评估PseMix的有效性与优越性。实验结果表明，PseMix能够帮助目前最先进的MIL网络刷新WSI分类性能。同时，该方法还能提升MIL模型在特殊测试场景下的泛化能力，并增强其对图像块遮挡和标签噪声的鲁棒性。源代码已开源至https://github.com/liupei101/PseMix。