  Multiple Instance Learning (MIL) methods have become increasingly popular for
classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.
Most MIL methods operate at a single WSI magnification, by processing all the
tissue patches. Such a formulation induces high computational requirements, and
constrains the contextualization of the WSI-level representation to a single
scale. A few MIL methods extend to multiple scales, but are computationally
more demanding. In this paper, inspired by the pathological diagnostic process,
we propose ZoomMIL, a method that learns to perform multi-level zooming in an
end-to-end manner. ZoomMIL builds WSI representations by aggregating
tissue-context information from multiple magnifications. The proposed method
outperforms the state-of-the-art MIL methods in WSI classification on two large
datasets, while significantly reducing the computational demands with regard to
Floating-Point Operations (FLOPs) and processing time by up to 40x.
