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Feng
Yang
,
PhD
Location: 
38A
/
09S909
Phone Number: (
301
827-1713
Expertise and Research Interests: 

Feng Yang, PhD, joined the Communication Engineering Branch at Lister Hill National Center for Biomedical Communications (LHNCBC) as a visiting scientist in October 2017. She is currently a visiting Professor in GuiZhou University. She had been an associate professor in Beijing Jiaotong University, Beijing, 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 research includes deep learning based disease detection and biomedical image processing and analysis.

Honors and Awards: 

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

Publications/Tools by Feng Yang: 
Yang F, Quizon N, Silamut K, Maude RJ, Jaeger S, Antani SK. Cascading YOLO: Automated Malaria Parasite Detection for Plasmodium Vivax in Thin Blood Smears. To be presented at SPIE Medical Imaging, Feb.18-20, 2020, Houston, USA.
Yu H, Yang F, Silamut R, Maude S, Jaeger S, Antani SK. Automatic Blood Smear Analysis with Artificial Intelligence and Smartphones. ASTMH 68th Annual Meeting, Washington DC, Nov. 20-24, 2019.
Yang F, Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G. Automated Parasite Classification of Malaria on Thick Blood Smears. ASTMH 67th Annual Meeting, New Orleans, LA, Oct. 28 – Nov. 1, 2018.
Yang F, Yu H, Silamut K, Maude RJ, Jaeger S, Antani SK. Parasite Detection in Thick Blood Smears Based on Customized Faster-RCNN. Proceedings of AIPR2019, Washington DC, USA, Oct 15-17, 2019.
Yang F, Yu H, Silamut K, Maude R, Jaeger S, Antani SK. Smartphone-Supported Malaria Diagnosis Based on Deep Learning. Proceedings of 10th Workshop on Machine Learning in Medical Imaging (MLMI 2019) in conjunction with MICCAI, Shenzhen, China, Oct 13-17, 2019.
Yang F, Yu H, Silamut K, Maude RJ, Jaeger S, Antani SK. Smartphone-Supported Malaria Diagnosis Based on Deep Learning. In: Suk HI., Liu M., Yan P., Lian C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science, vol 11861. Springer, Cham.
Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, Maude RJ, Jaeger S. Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE Journal of Biomedical and Health Informatics (JBHI). doi: 10.1109/JBHI.2019.2939121.
Yang F, Poostchi M, Yu H, Zhou Z, Silamut K, Yu J, Maude RJ, Jaeger S, Antani SK. Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears. IEEE J Biomed Health Inform. 2019 Sep 23. doi: 10.1109/JBHI.2019.2939121.
Jaeger S, Juarez-Espinosa OH, Candemir S, Poostchi M, Yang F, Kim L, Ding M, Folio LR, Antani SK, Gabrielian A, Hurt D, Rosenthal A, Thoma GR. Detecting drug-resistant tuberculosis in chest radiographs. Int J Comput Assist Radiol Surg. 2018 Dec;13(12):1915-1925. doi: 10.1007/s11548-018-1857-9. Epub 2018 Oct 3.
Yang F, Yu H, Poostchi M, Silamut K, Maude RJ, Jaeger S. Smartphone-Supported Automated Malaria Parasite Detection. SIIM conference on Machine Intelligence in Medical Imaging, 2018.

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