Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification. (arXiv:2306.16180v3 [cs.CV] UPDATED)

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 …

arxiv augmentation bag become classification current data face frameworks image images instance mil modeling multiple networks practice training