  The classification of gigapixel-sized whole slide images (WSIs), digital
representations of histological slides obtained via a high-resolution scanner,
faces significant challenges associated with the meticulous and time-consuming
nature of fine-grained labeling. While weakly-supervised multiple instance
learning (MIL) has emerged as a promising approach, current MIL methods are
constrained by their limited ability to leverage the wealth of information
embedded within unlabeled WSIs. This limitation often necessitates training MIL
feature aggregators from scratch after the feature extraction process,
hindering efficiency and accuracy. PreMix extends the general MIL framework by
pre-training the MIL aggregator with an intra-batch slide mixing approach.
Specifically, PreMix incorporates Barlow Twins Slide Mixing during
pre-training, enhancing its ability to handle diverse WSI sizes and maximizing
the utility of unlabeled WSIs. Combined with Mixup and Manifold Mixup during
fine-tuning, PreMix achieves a mean of 4.7% performance improvement over the
baseline MIL framework, the hierarchical image pyramid transformer (HIPT), on
the Camelyon16 dataset. The observed improvement across a range of active
learning acquisition functions and WSI-labeled training budgets highlights the
framework's adaptability to diverse datasets and varying resource constraints.
Ultimately, PreMix paves the way for more efficient and accurate WSI
classification under limited WSI-labeled datasets, encouraging the broader
adoption of unlabeled WSI data in histopathological research. The code is
available at https://anonymous.4open.science/r/PreMix
