Graph Attention Network For Microwave Imaging of Brain Anomaly

TL;DR

This paper introduces a graph attention network tailored for microwave imaging of brain anomalies, effectively incorporating physical array geometry to improve data efficiency and localization accuracy.

Contribution

It presents a novel graph-based neural network architecture that embeds the imaging array's physical structure, enhancing microwave imaging performance for brain anomaly detection.

Findings

The graph attention network outperforms traditional models in localization accuracy.

Incorporating physical geometry reduces data requirements.

The approach effectively handles multi-static array configurations.

Abstract

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.