PERSONNEL

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Feng Yang, PhD

Computational Health Research Branch

Contact Information
Building 38A - Lister Hill Center, 9S909
301.827.1713
feng.yang2@nih.gov


Expertise and Research Interests:

Feng Yang, PhD, joined the Lister Hill National Center for Biomedical Communications (LHNCBC), National Library of Medicine (NLM) in October 2017, is current a Research Fellow in NLM. She is also a visiting Professor in GuiZhou University. Dr. Yang had been working as a Principal Investigator, Associate Professor in Beijing Jiaotong University in China from 2012 to 2019. Dr. Yang received her PhD degree from National Institute of Applied Science (INSA Lyon) in France in 2011, and her B.S. and M.S. degrees from Northwestern Polytechnical University in China in 2005 and 2007, respectively. Her current research interests include machine learning and artificial intelligence based biomedical image processing and analysis. She has so far published more than 60 research papers, including 26 journal articles, 1 book chapters, and 36 conference proceedings.

Honors and Awards:

Dr. Feng Yang received the NLM Special Acts/Services Group Award in 2018.


Publications:

Yang F, Yu H, Kantipudi K, Rosenthal A, Hurt D, Antani S, Yaniv ZR, Jaeger S. Differentiating between Drug-Sensitive and Drug-Resistant Tuberculosis with Machine Learning for Clinical and Radiological Features. Quantitative Imaging in Medicine and Surgery, 0(0): 1–16, 2021. Publish Ahead of Print.

Niu P, Wang L, Xie B, Robini M, Boussel, L Douek P, Zhu Y, Yang F. Improved Image Reconstruction Using Multi-Energy Information in Spectral Photon-Counting CT. IEEE Access, vol. 9, pp. 97981-97989, 2021, doi: 10.1109/ACCESS.2021.3083505.

Karki M, Kantipudi K, Yu H, Yang F, Kassim Y, Yaniv Z,Jaeger S. Identifying Drug-Resistant Tuberculosis in Chest Radiographs: Evaluation of CNN Architectures and Training Strategies. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, accepted on July 15th, 2021, will be held virtually October 31 – November 4, 2021.

Kassim YM, Palaniappan K, Yang F, Poostchi M, Palaniappan N, Maude RJ, Antani S, Jaeger S. Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears. IEEE J Biomed Health Inform. 2021 May;25(5):1735-1746. doi: 10.1109/JBHI.2020.3034863. Epub 2021 May 11.

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