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Stefan
Jaeger
,
PhD
Staff Scientist
Location: 
38A
/
10N1003O
Phone Number: (
301
435-3198
Expertise and Research Interests: 

Dr. Stefan Jaeger is a staff scientist at the Lister Hill National Center for Biomedical Communications at the United States National Library of Medicine (NLM), which is part of the National Institutes of Health (NIH). He received his diploma from the University of Kaiserslautern and his PhD from the University of Freiburg, Germany, both in computer science. Dr. Jaeger has an international research background in academia as well as in industry. He has held research positions at Chinese Academy of Sciences, University of Maryland, University of Karlsruhe, Daimler, and others. At NLM, he supervises research on deep machine learning and data science for diagnosing infectious diseases, and conducts research into image informatics and artificial intelligence for clinical care and education. His research interests include machine learning, biomedical image analysis, artificial intelligence, medical informatics, and theoretical medicine. He has more than hundred publications in these areas, several of which received best paper awards and nominations, including two patents.

Professional Activities: 

Dr. Jaeger has acted as reviewer for national research councils and programs. He has served on the editorial boards of Quantitative Imaging in Medicine and Surgery, Electronic Journal of Emerging Infectious Diseases (China), and Electronic Letters on Computer Vision and Image Analysis (ELCVIA). He has also served as conference chair, keynote speaker, or program committee member for many conferences and workshops in his research area.

Honors and Awards: 
  • Award of Merit, National Institutes of Health, 2017.
  • HHS Innovation Ventures Award, U.S. Department of Health and Human Services, 2015.
  • Special Achievement Award, U.S. National Library of Medicine, 2015.
  • HHS-Ignite Pathway Team Award for Automatic X-ray Screening for Rural Areas, U.S. Department of Health and Human Services, 2014.
  • Certificate of Appreciation, Communications Engineering Branch, Lister Hill National Center for Biomedical Communications, 2014.
  • IAPR/ICDAR Young Investigator Award Nomination, International Association of Pattern Recognition, International Conference on Document Analysis and Recognition, 2007.
  • Best Student Paper, International Workshop on Frontiers in Handwriting Recognition (IWFHR), La   Baule, France;  Y. Li, Y. Zheng, D. Doermann, S. Jaeger. A New Algorithm for Detecting Text Line in Handwritten Documents, 2006.
  • Best Paper Nomination, International Conference on Document Analysis and Recognition (ICDAR), Seoul, Korea: S. Jaeger, H. Ma, D. Doermann. Identifying Script on Word-Level with Informational  Confidence, 2005.
  • Research Fellowship, New Energy and Industrial Technology Development Organization (NEDO), Japan, Nov. 2000 – March 2003.
  • PhD Thesis Award, German Research Centers for Artificial Intelligence, 1999.
  • Daimler-Benz Graduate Fellow, Daimler-Benz Research Center, Ulm, Germany, 1994 –1998.
Publications/Tools by Stefan Jaeger: 
Zheng Q, Lu Y, Lure F, Jaeger S, Lu P. Clinical and radiological features of novel coronavirus pneumonia. Journal of X-Ray Science and Technology, vol. 28, no. 3, pp. 391-404, 2020.
Jaeger S. The Golden Ratio of Learning and Momentum. arXiv:2006.04751 [cs.LG], 2020.
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.
Cheng P, Lu P, Wang P, Zhou W, Yu W, Jaeger S, Li J, Wu T, Ke X, Zheng B, Antani SK, Candemir S, Quan S, Lure F, Li H, Guo L. Applying Deep Learning and Radiomics to Determine Biological Lung and Heart Age from Chest Radiographs. Chinese Congress of Radiology.
Wang X, Guan Y, Lu P, Cheng G, Zhou W, Jaeger S, Zhen B, Antani SK, Yin X, Yu W, Guo L, Quan S, Lure F, Hurt D, Gabrielian A, Li H, Ke X. Screening of Tuberculosis in a TB High-burden Large Rural Region in China with Deep Learning Multi-modality Artificial Intelligence. Chinese Congress of Radiology.
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.
Rajaraman S, Jaeger S, Antani SK. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ. doi: 10.7717/peerj.6977.
Lure F, Jaeger S, Cheng G, Li H, Lu P, Yu W, Kung J, Guan Y. Applying Multi-modality Artificial Intelligence for Screening of Tuberculosis in a TB High-burden Large Rural Region in China TBScience, 50th Union World Conference on Lung Health, Hyderabad, India.
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.

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