A direct learning approach for detection of hotspots in microwave hyperthermia treatments.

This paper presents a computational study for detecting whether the temperature values of the breast tissues are exceeding a threshold using deep learning (DL) during microwave hyperthermia (MH) treatments. The proposed model has a deep convolutional encoder-decoder architecture, which gets differential scattered field data as input and gives an image showing the cells exceeding the threshold. The data are generated by an in-house data generator, which mimics temperature distribution in the MH problem.

Monitoring of intracerebral hemorrhage with a linear microwave imaging algorithm.

Intracerebral hemorrhage is a life-threatening condition where conventional imaging modalities such as CT and MRI are indispensable in diagnosing. Nevertheless, monitoring the evolution of intracerebral hemorrhage still poses a technological challenge. We consider continuous monitoring of intracerebral hemorrhage in this context and present a differential microwave imaging scheme based on a linearized inverse scattering.