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• W3133041489 abstract "Magnetic induction tomography (MIT) is an efficient solution for long-term brain disease monitoring, which focuses on reconstructing bio-impedance distribution inside the human brain using non-intrusive electromagnetic fields. However, high-quality brain image reconstruction remains challenging since reconstructing images from the measured weak signals is a highly non-linear and ill-conditioned problem. In this work, we propose a generative adversarial network (GAN) enhanced MIT technique, named MITNet, based on a complex convolutional neural network (CNN). The experimental results on the real-world dataset validate the performance of our technique, which outperforms the state-of-art method by 25.27%." @default.
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• W3133041489 date "2021-02-15" @default.
• W3133041489 modified "2023-09-25" @default.
• W3133041489 title "MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN" @default.
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• W3133041489 doi "https://doi.org/10.48550/arxiv.2102.07911" @default.
• W3133041489 hasPublicationYear "2021" @default.
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