Feature-Based Inversion Using Variational Autoencoder for Electrical Impedance Tomography

Author:

Lin Zhichao1ORCID,Guo Rui1ORCID,Zhang Ke1ORCID,Li Maokun1ORCID,Yang Fan1ORCID,Xu Shenheng1ORCID,Liu Dong2ORCID,Abubakar Aria3ORCID

Affiliation:

1. Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China

2. CAS Key Laboratory of Microscale Magnetic Resonance and School of Physical Sciences, University of Science and Technology of China, Hefei, China

3. Schlumberger, Houston, TX, USA

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Institute for Precision Medicine, Tsinghua University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

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1. Spatio-Temporal Classification of Lung Ventilation Patterns Using 3D EIT Images: A General Approach for Individualized Lung Function Evaluation;IEEE Journal of Biomedical and Health Informatics;2024-01

2. Physics Embedded Weight-Sharing Neural Network for Electrical Impedance Tomography;2023 IEEE Conference on Antenna Measurements and Applications (CAMA);2023-11-15

3. Deep generative learning for exploration in large electrochemical impedance dataset;Engineering Applications of Artificial Intelligence;2023-11

4. Feature-based magnetotelluric inversion by variational autoencoder using a subdomain encoding scheme;GEOPHYSICS;2023-10-16

5. Joint Inversion of Electrical Impedance, Microwave and Ultrasonic Data With Structural Feature Fusion for Human Thorax Imaging;2023 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO);2023-06-28

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