  Whole slide image (WSI) assessment is a challenging and crucial step in
cancer diagnosis and treatment planning. WSIs require high magnifications to
facilitate sub-cellular analysis. Precise annotations for patch- or even
pixel-level classifications in the context of gigapixel WSIs are tedious to
acquire and require domain experts. Coarse-grained labels, on the other hand,
are easily accessible, which makes WSI classification an ideal use case for
multiple instance learning (MIL). In our work, we propose a novel
embedding-based Dual-Query MIL pipeline (DQ-MIL). We contribute to both the
embedding and aggregation steps. Since all-purpose visual feature
representations are not yet available, embedding models are currently limited
in terms of generalizability. With our work, we explore the potential of
dynamic meta-embedding based on cutting-edge self-supervised pre-trained models
in the context of MIL. Moreover, we propose a new MIL architecture capable of
combining MIL-attention with correlated self-attention. The Dual-Query
Perceiver design of our approach allows us to leverage the concept of
self-distillation and to combine the advantages of a small model in the context
of a low data regime with the rich feature representation of a larger model. We
demonstrate the superior performance of our approach on three histopathological
datasets, where we show improvement of up to 10% over state-of-the-art
approaches.

Computer Vision and Pattern Recognition