Matches in SemOpenAlex for { <https://semopenalex.org/work/W4311331221> ?p ?o ?g. }
- W4311331221 abstract "Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future." @default.
- W4311331221 created "2022-12-25" @default.
- W4311331221 creator A5035701638 @default.
- W4311331221 creator A5053886260 @default.
- W4311331221 creator A5056640178 @default.
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- W4311331221 creator A5071244617 @default.
- W4311331221 creator A5076150103 @default.
- W4311331221 creator A5082923149 @default.
- W4311331221 creator A5084878440 @default.
- W4311331221 date "2022-12-14" @default.
- W4311331221 modified "2023-10-17" @default.
- W4311331221 title "Advances of deep learning in electrical impedance tomography image reconstruction" @default.
- W4311331221 cites W1638432468 @default.
- W4311331221 cites W1901129140 @default.
- W4311331221 cites W1928301487 @default.
- W4311331221 cites W1969648880 @default.
- W4311331221 cites W1973707019 @default.
- W4311331221 cites W1976134583 @default.
- W4311331221 cites W1979994801 @default.
- W4311331221 cites W1994794867 @default.
- W4311331221 cites W1998016850 @default.
- W4311331221 cites W2004061853 @default.
- W4311331221 cites W2005958698 @default.
- W4311331221 cites W2026069135 @default.
- W4311331221 cites W2083598195 @default.
- W4311331221 cites W2088193537 @default.
- W4311331221 cites W2105227924 @default.
- W4311331221 cites W2106265771 @default.
- W4311331221 cites W2113761410 @default.
- W4311331221 cites W2118956405 @default.
- W4311331221 cites W2120625383 @default.
- W4311331221 cites W2125619995 @default.
- W4311331221 cites W2129720819 @default.
- W4311331221 cites W2134021512 @default.
- W4311331221 cites W2151220638 @default.
- W4311331221 cites W2284794734 @default.
- W4311331221 cites W2542227116 @default.
- W4311331221 cites W2547655816 @default.
- W4311331221 cites W2548277151 @default.
- W4311331221 cites W2557452911 @default.
- W4311331221 cites W2579819041 @default.
- W4311331221 cites W2734887256 @default.
- W4311331221 cites W2735392049 @default.
- W4311331221 cites W2737383373 @default.
- W4311331221 cites W2740337843 @default.
- W4311331221 cites W2754288225 @default.
- W4311331221 cites W2759764994 @default.
- W4311331221 cites W2760455187 @default.
- W4311331221 cites W2767290858 @default.
- W4311331221 cites W2795198316 @default.
- W4311331221 cites W2800214139 @default.
- W4311331221 cites W2803200748 @default.
- W4311331221 cites W2810180541 @default.
- W4311331221 cites W2883561321 @default.
- W4311331221 cites W2886849382 @default.
- W4311331221 cites W2890531139 @default.
- W4311331221 cites W2892265014 @default.
- W4311331221 cites W2896048271 @default.
- W4311331221 cites W2896344704 @default.
- W4311331221 cites W2897245162 @default.
- W4311331221 cites W2904214450 @default.
- W4311331221 cites W2905450017 @default.
- W4311331221 cites W2910112947 @default.
- W4311331221 cites W2920929174 @default.
- W4311331221 cites W2921393954 @default.
- W4311331221 cites W2931476234 @default.
- W4311331221 cites W2948801778 @default.
- W4311331221 cites W2950567011 @default.
- W4311331221 cites W2954414184 @default.
- W4311331221 cites W2955007489 @default.
- W4311331221 cites W2955486757 @default.
- W4311331221 cites W2955580332 @default.
- W4311331221 cites W2956685331 @default.
- W4311331221 cites W2962092236 @default.
- W4311331221 cites W2962843773 @default.
- W4311331221 cites W2963231761 @default.
- W4311331221 cites W2968647901 @default.
- W4311331221 cites W2976970117 @default.
- W4311331221 cites W2979645297 @default.
- W4311331221 cites W2982393726 @default.
- W4311331221 cites W2990050904 @default.
- W4311331221 cites W2992278619 @default.
- W4311331221 cites W2994953704 @default.
- W4311331221 cites W2996058900 @default.
- W4311331221 cites W2997422821 @default.
- W4311331221 cites W2998105502 @default.
- W4311331221 cites W2998488905 @default.
- W4311331221 cites W2999516274 @default.
- W4311331221 cites W3003741466 @default.
- W4311331221 cites W3005368325 @default.
- W4311331221 cites W3011836624 @default.
- W4311331221 cites W3013193552 @default.
- W4311331221 cites W3018649586 @default.
- W4311331221 cites W3025051614 @default.
- W4311331221 cites W3030487003 @default.
- W4311331221 cites W3035181684 @default.
- W4311331221 cites W3035905600 @default.
- W4311331221 cites W3037817844 @default.
- W4311331221 cites W3039123483 @default.