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2531por Hyun, Dongwoon, Wiacek, Alycen, Goudarzi, Sobhan, Rothlübbers, Sven, Asif, Amir, Eickel, Klaus, Eldar, Yonina C., Huang, Jiaqi, Mischi, Massimo, Rivaz, Hassan, Sinden, David, van Sloun, Ruud J. G., Strohm, Hannah, Bell, Muyinatu A. Lediju“…This article introduces the largest known international database of ultrasound channel data and describes the associated evaluation methods that were initially developed for the challenge on ultrasound beamforming with deep learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium. We summarize the challenge results and present qualitative and quantitative assessments using both the initially closed CUBDL evaluation test dataset (which was crowd-sourced from multiple groups around the world) and additional in vivo breast ultrasound data contributed after the challenge was completed. …”
Publicado 2021
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Online Artículo Texto -
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2534por Qi, Ailiang, Zhao, Dong, Yu, Fanhua, Heidari, Ali Asghar, Wu, Zongda, Cai, Zhennao, Alenezi, Fayadh, Mansour, Romany F., Chen, Huiling, Chen, Mayun“…Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. …”
Publicado 2022
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Online Artículo Texto -
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2539“…Crowdsourcing learning (Bonald and Combes 2016; Dawid and Skene, J R Stat Soc: Series C (Appl Stat), 28(1):20–28 1979; Karger et al. 2011; Li et al, IEEE Trans Knowl Data Eng, 28(9):2296–2319 2016; Liu et al. 2012; Schlagwein and Bjorn-Andersen, J Assoc Inform Syst, 15(11):3 2014; Zhang et al. 2014) plays an increasingly important role in the era of big data (Liu et al., IEEE Trans Syst Man Cybern: Syst, 48(12): 451–2461, 2017; Zhang et al. 2014) due to its ability to easily solve large-scale data annotations (Musen et al., J Amer Med Informs Assoc, 22(6):1148–1152 2015). …”
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Enlace del recurso
Enlace del recurso
Online Artículo Texto -
2540