Ultrasonographic Segmentation of Fetal Lung with Deep Learning

Journal of Biosciences and Medicines

ISSN Print: 2327-5081
ISSN Online: 2327-509X
www.scirp.net/journal/jbm
E-mail: [email protected]

Citations

"Ultrasonographic Segmentation of Fetal Lung with Deep Learning"
written by Jintao Yin, Jiawei Li, Qinghua Huang, Yucheng Cao, Xiaoqian Duan, Bing Lu, Xuedong Deng, Qingli Li, Jiangang Chen,
published by Journal of Biosciences and Medicines, Vol.9 No.1, 2021

has been cited by the following article(s):

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[1]	Recent and future applications of artificial intelligence in obstetric ultrasound examination
	Artificial Intelligence and …, 2024

[2]	Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
	Balcells, VM Campello… - Scientific Reports, 2023

[3]	Ultrasonic evaluation of fetal lung development using deep learning with graph
	Displays, 2023

[4]	Computer Aided Diagnosis for COVID-19 in CT Images Utilizing Transfer Learning and Attention Mechanism
	Journal of Shanghai Jiaotong …, 2023

[5]	Performance Analysis of Squeezenet and Densenet on Fetal Brain MRI Dataset
	2022 6th …, 2022

[6]	Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries
	Balcells, VM Campello… - arXiv preprint arXiv …, 2022

[7]	Classification of common fetal anatomical planes from ultrasound imaging using dempster shafer theory and deep learning
	2022

[8]	Transfer learning U-Net deep learning for lung ultrasound segmentation
	arXiv preprint arXiv:2110.02196, 2021

[1]	Ultrasonic evaluation of fetal lung development using deep learning with graph Displays, 2023 DOI:10.1016/j.displa.2023.102451

[2]	Computer Aided Diagnosis for COVID-19 in CT Images Utilizing Transfer Learning and Attention Mechanism Journal of Shanghai Jiaotong University (Science), 2023 DOI:10.1007/s12204-023-2646-3

[3]	Performance Analysis of Squeezenet and Densenet on Fetal Brain MRI Dataset 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022 DOI:10.1109/ICCMC53470.2022.9753874

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