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Peifang Liu
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2020 – today
- 2024
- [j4]Peifang Liu
, Jiang Guo, Fangqing Zhang, Ye Zou, Junjie Tang:
Multi-objective optimization of power networks integrating electric vehicles and wind energy. Intell. Syst. Appl. 24: 200452 (2024) - [j3]Peifang Liu, Xueliang Liu:
Complementary expert balanced learning for long-tail cross-modal retrieval. Multim. Syst. 30(2): 113 (2024) - 2022
- [c4]Heather M. Whitney, Yu Ji, Hui Li, Peifang Liu, Maryellen L. Giger:
Effect of different molecular subtype reference standards in AI training: implications for DCE-MRI radiomics of breast cancers. Computer-Aided Diagnosis 2022 - 2020
- [j2]Heather M. Whitney
Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Method. Proc. IEEE 108(1): 163-177 (2020) - [i1]Li Xiao, Cheng Zhu, Junjun Liu, Chunlong Luo, Peifang Liu, Yi Zhao:
Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography. CoRR abs/2003.01109 (2020)
2010 – 2019
- 2019
- [c3]Heather M. Whitney, Yu Ji, Hui Li, Alexandra Edwards, John Papaioannou, Peifang Liu, Maryellen L. Giger:
Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2, 397 breast lesions. Computer-Aided Diagnosis 2019: 109501A - [c2]Rachel Anderson, Hui Li, Yu Ji, Peifang Liu, Maryellen L. Giger:
Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer. Computer-Aided Diagnosis 2019: 1095006 - [c1]Li Xiao, Cheng Zhu, Junjun Liu, Chunlong Luo, Peifang Liu, Yi Zhao:
Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography. MICCAI (6) 2019: 468-476 - 2015
- [j1]Qinwei Li, Xia Xiao, Liang Wang, Hang Song
Direct Extraction of Tumor Response Based on Ensemble Empirical Mode Decomposition for Image Reconstruction of Early Breast Cancer Detection by UWB. IEEE Trans. Biomed. Circuits Syst. 9(5): 710-724 (2015)
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