  Cancer subtyping is one of the most challenging tasks in digital pathology,
where Multiple Instance Learning (MIL) by processing gigapixel whole slide
images (WSIs) has been in the spotlight of recent research. However, MIL
approaches do not take advantage of inter- and intra-magnification information
contained in WSIs. In this work, we present GRASP, a novel graph-structured
multi-magnification framework for processing WSIs in digital pathology. Our
approach is designed to dynamically emulate the pathologist's behavior in
handling WSIs and benefits from the hierarchical structure of WSIs. GRASP,
which introduces a convergence-based node aggregation instead of traditional
pooling mechanisms, outperforms state-of-the-art methods over two distinct
cancer datasets by a margin of up to 10% balanced accuracy, while being 7 times
smaller than the closest-performing state-of-the-art model in terms of the
number of parameters. Our results show that GRASP is dynamic in finding and
consulting with different magnifications for subtyping cancers and is reliable
and stable across different hyperparameters. The model's behavior has been
evaluated by two expert pathologists confirming the interpretability of the
model's dynamic. We also provide a theoretical foundation, along with empirical
evidence, for our work, explaining how GRASP interacts with different
magnifications and nodes in the graph to make predictions. We believe that the
strong characteristics yet simple structure of GRASP will encourage the
development of interpretable, structure-based designs for WSI representation in
digital pathology. Furthermore, we publish two large graph datasets of rare
Ovarian and Bladder cancers to contribute to the field.
