The following publications are possibly variants of this publication:
- Diagnosis of Significant Liver Fibrosis by Using a DCNN Model With Fusion of Features From US B-Mode Image and Nakagami Parametric Map: An Animal StudyQiang Liu, Zhong Liu, Wencong Xu, Huiying Wen, Ming Dai, Xin Chen 0025. access, 9:89300-89310, 2021. [doi]
- GLCV-NET: An automatic diagnosis system for advanced liver fibrosis using global-local cross view in B-mode ultrasound imagesBianzhe Wu, Ze-Rong Huang, Jinglin Liang, Hong Yang, Wei Wang 0181, Shuangping Huang, Li-Da Chen, Qinghua Huang. cmpb, 257:108440, 2024. [doi]
- 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 StudyHaiming Ai, Yong Huang, Dar-In Tai, Po-Hsiang Tsui, Zhuhuang Zhou. sensors, 24(17):5513, September 2024. [doi]
- Adversarial robustness of deep neural networks: A survey from a formal verification perspectiveMeng, Mark Huasong, Bai, Guangdong, Teo, Sin Gee, Hou, Zhe, Xiao, Yan, Lin, Yun, Dong, Jin Song. IEEE Transactions on Dependable and Secure Computing, , 2022.
- Automatic ROI Segmentation in B-Mode Ultrasound Image for Liver Fibrosis ClassificationNan-Han Lu, Chung Ming Kuo, Hueisch-Jy Ding. isbast 2013: 10-13 [doi]
- Ultrasound Liver Fibrosis Diagnosis Using Multi-indicator Guided Deep Neural NetworksJiali Liu, Wenxuan Wang, Tianyao Guan, Ningbo Zhao, Xiaoguang Han, Zhen Li. miccai 2019: 230-237 [doi]