  The abundance of information present in Whole Slide Images (WSIs) makes them
useful for prognostic evaluation. A large number of models utilizing a
pretrained ResNet backbone have been released and employ various feature
aggregation techniques, primarily based on Multiple Instance Learning (MIL). By
leveraging the recently released UNI feature extractor, existing models can be
adapted to achieve higher accuracy, which paves the way for more robust
prognostic tools in digital pathology.
