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 data memorization nature 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 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 adaptive to MIL, 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. Test results show that PseMix could often improve the performance of MIL networks in WSI classification. Besides, it could also boost the generalization capacity of MIL models, and promote their robustness to patch occlusion and noisy labels. 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，多实例学习（MIL）已成为全切片图像（WSI）分类中最重要框架之一。当前实践中，多数MIL网络在训练时面临两个不可避免的问题：i) WSI数据不足，ii) 神经网络固有的数据记忆特性。这些问题可能阻碍MIL模型进行充分高效训练，抑制WSI分类模型性能的持续提升。受Mixup基本思想启发，本文提出伪包混合增强（PseMix）数据扩充方案以改进MIL模型训练。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。在伪包协同作用下，PseMix实现了Mixup策略中关键的尺寸对齐与语义对齐。此外，该方法被设计为自适应MIL的高效解耦方案，既无需耗时操作也不依赖MIL模型预测。通过专门设计的对比实验与消融研究评估了PseMix的有效性与优势。测试结果表明，PseMix能有效提升MIL网络在WSI分类中的性能，同时增强MIL模型的泛化能力，并提升其对图像块遮挡与噪声标签的鲁棒性。源代码已开源至https://github.com/liupei101/PseMix。