Nowadays, there is an increasing need for effective, reliable, fast, and low-cost technologies to perform biomedical electromagnetic (EM) vision, prognostics, and diagnostics. In this framework, the members of the ELEDIA Research Center network developed several techniques based on artificial intelligence (AI) and machine learning (ML) for the real-time inversion of data from heterogeneous sensing technologies, as well as multi-resolution approaches adaptively refining the resolution within the detected regions of interest. More in detail, the ELEDIA research areas in the field include
- Early detection, localization, and characterization of brain pathologies (e.g., tumors, Alzheimer’s disease);
- Reliable classification of brain strokes (i.e., hemorrhagic/ischemic) to select the most suitable treatment for the patient;
- Continuous monitoring/follow-up (also remotely) of chest organs (e.g., lungs) to track their health status, for instance by checking the air/liquid content of the lungs of patients recovered in intensive care units;
- Early detection of breast cancer and skin tumors which cannot be easily detected by visual inspection through non-invasive and non-painful techniques.
Within this framework, the members of the ELEDIA Research Center network developed several techniques based on AI and the learning-by-examples (LBE) paradigm enabling the real-time inversion of data collected over an external observation domain with heterogeneous sensing technologies (e.g., microwaves, electrical impedance tomography, and terahertz radiation). Moreover, innovative strategies have been introduced to exploit the a-priori information on the imaged domain by means of suitable differential formulations of the scattering problem at hand, along with multi-resolution approaches aimed at adaptively refining the resolution within the detected regions of interest (RoIs).
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