  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.
