  Electromagnetic medical imaging in the microwave regime is a hard problem
notorious for 1) instability 2) under-determinism. This two-pronged problem is
tackled with a two-pronged solution that uses double compression to maximally
utilizing the cheap unlabelled data to a) provide a priori information required
to ease under-determinism and b) reduce sensitivity of inference to the input.
The result is a stable solver with a high resolution output. DeepHead is a
fully data-driven implementation of the paradigm proposed in the context of
microwave brain imaging. It infers the dielectric distribution of the brain at
a desired single frequency while making use of an input that spreads over a
wide band of frequencies. The performance of the model is evaluated with both
simulations and human volunteers experiments. The inference made is juxtaposed
with ground-truth dielectric distribution in simulation case, and the golden
MRI / CT imaging modalities of the volunteers in real-world case.
