A Physics-Driven AI Approach for Microwave Imaging of Breast Tumors

TL;DR

This paper introduces a physics-driven AI method for microwave imaging that improves breast tumor detection by leveraging a differential inverse scattering formulation and a global optimization approach, validated on synthetic and experimental data.

Contribution

It presents a novel AI-based microwave imaging technique using a differential inverse scattering formulation combined with a physics-driven optimization strategy, advancing breast tumor diagnosis.

Findings

Effective in synthetic data tests

Robust against noise and model inaccuracies

Validated with experimental data

Abstract

In this paper, an innovative microwave imaging (MI) approach for breast tumor diagnosis is proposed that employs a differential formulation of the inverse scattering problem (ISP) at hand to exploit arbitrary-fidelity priors on the inhomogeneous reference/healthy tissues. The quantitative imaging of the unknown tumor is then rephrased into a global optimization problem, which is efficiently solved with an ad-hoc physics-driven artificial intelligence (AI) strategy inspired by the concepts and guidelines of the System-by-Design (SbD) paradigm. The effectiveness, the robustness, the reliability, and the efficiency of the proposed method are assessed against both synthetic and experimental data.