Integrating multiscale topology in digital pathology with pyramidal
  graph convolutional networks
Victor Ibañez Przemyslaw Szostak Quincy Wong Konstanty Korski Samaneh Abbasi-Sureshjani Alvaro Gomariz
Graph convolutional networks (GCNs) have emerged as a powerful alternative to
multiple instance learning with convolutional neural networks in digital
pathology, offering superior handling of structural information across various
spatial ranges - a crucial aspect of learning from gigapixel H&E-stained whole
slide images (WSI). However, graph message-passing algorithms often suffer from
oversmoothing when aggregating a large neighborhood. Hence, effective modeling
of multi-range interactions relies on the careful construction of the graph.
Our proposed multi-scale GCN (MS-GCN) tackles this issue by leveraging
information across multiple magnification levels in WSIs. MS-GCN enables the
simultaneous modeling of long-range structural dependencies at lower
magnifications and high-resolution cellular details at higher magnifications,
akin to analysis pipelines usually conducted by pathologists. The
architecture's unique configuration allows for the concurrent modeling of
structural patterns at lower magnifications and detailed cellular features at
higher ones, while also quantifying the contribution of each magnification
level to the prediction. Through testing on different datasets, MS-GCN
demonstrates superior performance over existing single-magnification GCN
methods. The enhancement in performance and interpretability afforded by our
method holds promise for advancing computational pathology models, especially
in tasks requiring extensive spatial context.