Electrical impedance tomography-based abdominal obesity estimation using deep learningMS54

We propose a deep learning method for abdominal electrical impedance tomography (EIT) to estimate abdominal obesity. EIT for evaluating abdominal obesity is a challenging problem that is an ill-posed absolute imaging problem. The proposed method allows to find an useful solution within a restricted admissible set, accounting for prior information on abdominal anatomy. It found that a specially designed training data used in the deep learning process reduces the ill-posedness in the absolute EIT problem.

This presentation is part of Minisymposium “MS54 - Hybrid Approaches that Combine Deterministic and Statistical Regularization for Applied Inverse Problems (4 parts)
organized by: Cristiana Sebu (University of Malta) , Taufiquar Khan (Clemson University) .

Jin Keun Seo (Yonsei University)
Kyounghun Lee (Yonsei University)
Minha Yoo (National Institute for Mathematical Sciences)
abdominal obesity, deep learning, electrical impedance tomography, image reconstruction, inverse problems