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Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.

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Title: Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.
Authors: Ai, Haiming1 (AUTHOR) aihm@bjou.edu.cn, Huang, Yong2 (AUTHOR) huangy@emails.bjut.edu.cn, Tai, Dar-In3 (AUTHOR) tai48978@cgmh.org.tw, Tsui, Po-Hsiang4,5,6,7 (AUTHOR) tsuiph@mail.cgu.edu.tw, Zhou, Zhuhuang2 (AUTHOR) tsuiph@mail.cgu.edu.tw
Source: Sensors (14248220). Sep2024, Vol. 24 Issue 17, p5513. 19p.
Database: Academic Search Ultimate
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=179646455
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        Value: 10.3390/s24175513
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        Text: English
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      – TitleFull: Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.
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              M: 09
              Text: Sep2024
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