Yu, Jinhua

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Zheng, X., Yao, Z., Huang, Y., Yu, Y., Wang, Y., Liu, Y., Mao, R., Li, F., Xiao, Y., Wang, Y., et al. (2020) · Nature Communications

Radiomics and Machine Learning in Medical ImagingUltrasound Imaging and Elastography

Cited by 607

Segmentation of fetal left ventricle in echocardiographic sequences based on dynamic convolutional neural networks

Yu, L., Guo, Y., Wang, Y., Yu, J., & Chen, P. (2017) · IEEE Transactions on Biomedical Engineering

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 96

Automatic Detection of Standard Sagittal Plane in the First Trimester of Pregnancy Using 3-D Ultrasound Data

Nie, S., Yu, J., Chen, P., Wang, Y., & Zhang, J. Q. (2017) · Ultrasound in Medicine & Biology

Medical Image Segmentation TechniquesPrenatal Screening and Diagnostics

Cited by 42

A hierarchical model for automatic nuchal translucency detection from ultrasound images

Deng, Y., Wang, Y., Chen, P., & Yu, J. (2012) · Computers in Biology and Medicine

Medical Image Segmentation TechniquesPrenatal Screening and Diagnostics

Cited by 31

A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images

Chen, P., Chen, Y., Deng, Y., Wang, Y., He, P., Lv, X., & Yu, J. (2020) · International Journal of Computer Assisted Radiology and Surgery

Medical Image Segmentation TechniquesRadiomics and Machine Learning in Medical Imaging

Cited by 15