  Multiple instance learning (MIL) is the most widely used framework in
computational pathology, encompassing sub-typing, diagnosis, prognosis, and
more. However, the existing MIL paradigm typically requires an offline instance
feature extractor, such as a pre-trained ResNet or a foundation model. This
approach lacks the capability for feature fine-tuning within the specific
downstream tasks, limiting its adaptability and performance. To address this
issue, we propose a Re-embedded Regional Transformer (R$^2$T) for re-embedding
the instance features online, which captures fine-grained local features and
establishes connections across different regions. Unlike existing works that
focus on pre-training powerful feature extractor or designing sophisticated
instance aggregator, R$^2$T is tailored to re-embed instance features online.
It serves as a portable module that can seamlessly integrate into mainstream
MIL models. Extensive experimental results on common computational pathology
tasks validate that: 1) feature re-embedding improves the performance of MIL
models based on ResNet-50 features to the level of foundation model features,
and further enhances the performance of foundation model features; 2) the
R$^2$T can introduce more significant performance improvements to various MIL
models; 3) R$^2$T-MIL, as an R$^2$T-enhanced AB-MIL, outperforms other latest
methods by a large margin.The code is available at:
https://github.com/DearCaat/RRT-MIL.
