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 the classification performance on WSIs. Besides, it could also boost the generalization ability 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：鉴于处理千兆像素图像的特殊场景，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。当前实践中，多数MIL网络在训练过程中常面临两个不可避免的问题：i）WSI数据不足，以及ii）神经网络固有的样本记忆倾向。这些问题可能阻碍MIL模型进行充分且高效的训练，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合增强（PseMix）数据扩增方案以改进MIL模型训练。该方案通过伪包将面向通用图像的Mixup策略泛化至特殊WSI场景，从而应用于基于MIL的WSI分类。在伪包协同下，PseMix实现了Mixup策略中关键的尺寸对齐与语义对齐。此外，该方法被设计为高效且解耦的方案，既不涉及耗时操作，也不依赖MIL模型预测。我们特别设计了对比实验与消融研究以评估PseMix的有效性与优势。实验结果表明，PseMix能协助最先进的MIL网络刷新WSI分类性能，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。