  So far, numerous learned models have been pressed to use in microwave imaging
problems. These models however, are oblivious to the imaging geometry. It has
always been hard to bake the physical setup of the imaging array into the
structure of the network, resulting in a data-intensive models that are not
practical. This work put forward a graph formulation of the microwave imaging
array. The architectures proposed is made cognizant of the physical setup,
allowing it to incorporate the symmetries, resulting in a less data
requirements. Graph convolution and attention mechanism is deployed to handle
the cases of fully-connected graphs corresponding to multi-static arrays. The
graph-treatment of the problem is evaluated on experimental setup in context of
brain anomaly localization with microwave imaging.
