Classification of stroke using neural networks in electrical impedance tomography
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Publication:5132268
DOI10.1088/1361-6420/abbdcdzbMath1451.92178arXiv2004.06546OpenAlexW3104579665MaRDI QIDQ5132268
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Publication date: 10 November 2020
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2004.06546
Biomedical imaging and signal processing (92C55) Neural networks for/in biological studies, artificial life and related topics (92B20)
Related Items (6)
Neural networks for classification of strokes in electrical impedance tomography on a 3D head model ⋮ Machine learning enhanced electrical impedance tomography for 2D materials ⋮ Immersed boundary method for the complete electrode model in electrical impedance tomography ⋮ Imaging of conductivity distribution based on a combined reconstruction method in brain electrical impedance tomography ⋮ A direct reconstruction algorithm for the anisotropic inverse conductivity problem based on Calderón’s method in the plane ⋮ Simultaneous Reconstruction of Conductivity, Boundary Shape, and Contact Impedances in Electrical Impedance Tomography
Uses Software
Cites Work
- A global uniqueness theorem for an inverse boundary value problem
- Propagation and recovery of singularities in the inverse conductivity problem
- Regularized D-bar method for the inverse conductivity problem
- Dynamic optimized priors for D-bar reconstructions of human ventilation using electrical impedance tomography
- Calderón's inverse conductivity problem in the plane
- Fifty Years of Mathematical Physics
- Numerical solution of the $\mathbb R$-linear Beltrami equation
- Scattering and inverse scattering for first order systems
- Existence and Uniqueness for Electrode Models for Electric Current Computed Tomography
- Linear and Nonlinear Inverse Problems with Practical Applications
- Complete Electrode Model of Electrical Impedance Tomography: Approximation Properties and Characterization of Inclusions
- Solving inverse problems using data-driven models
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