  Modern histopathological image analysis relies on the segmentation of cell
structures to derive quantitative metrics required in biomedical research and
clinical diagnostics. State-of-the-art deep learning approaches predominantly
apply convolutional layers in segmentation and are typically highly customized
for a specific experimental configuration; often unable to generalize to
unknown data. As the model capacity of classical convolutional layers is
limited by a finite set of learned kernels, our approach uses a graph
representation of the image and focuses on the node transitions in multiple
magnifications. We propose a novel architecture for semantic segmentation of
cell nuclei robust to differences in experimental configuration such as
staining and variation of cell types. The architecture is comprised of a novel
neuroplastic graph attention network based on residual graph attention layers
and concurrent optimization of the graph structure representing multiple
magnification levels of the histopathological image. The modification of graph
structure, which generates the node features by projection, is as important to
the architecture as the graph neural network itself. It determines the possible
message flow and critical properties to optimize attention, graph structure,
and node updates in a balanced magnification loss. In experimental evaluation,
our framework outperforms ensembles of state-of-the-art neural networks, with a
fraction of the neurons typically required, and sets new standards for the
segmentation of new nuclei datasets.
