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Zhiyun
Xue
,
PhD
Staff Scientist
Location: 
38A
/
10S1008
Phone Number: (
301
827-4939
Expertise and Research Interests: 

Dr. Zhiyun (Jaylene) Xue is a Staff Scientist at the Lister Hill National Center for Biomedical Communications (LHC) at the National Library of Medicine (NLM). She obtained her Ph.D. from Lehigh University USA, M.S. and B.S. from Tsinghua University, China. Dr. Xue has been working at LHC since 2006 on a number of medical imaging informatics projects. By applying her knowledge and expertise in the fields of machine learning, image processing, and computer vision to analyze biomedical images in different modalities, Dr. Xue puts her R&D efforts in those projects with the goals of advancing the research in biomedical informatics and data science, assisting clinicians at the point-of-care, improving the health of the people, and addressing the needs of the underserved population. One project she is passionate about and is currently devoting herself to is the project of automatic image analysis for cervical cancer screening, which aims to reduce the mortality of cervical cancer, one of the most common cancers among women.

Honors and Awards: 

Dr. Xue received the HHS Ignite Group Award in 2014, the NLM Special Acts/Services Group Award in 2011-2014 and 2017.

Publications/Tools by Zhiyun Xue: 
Guo P, Xue Z, Mtema Z, Yeates K, Ginsburg O, Demarco M, Long LR, Schiffman M, Antani SK. 2. Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening. Diagnostics (Basel) 2020 Jul 3;10(7):451.
Xue Z, Novetsky A, Einstein M, Marcus J, Befano B, Guo P, Demarco M, Wenzenten N, Long LR, Schiffman M, Antani SK. A demonstration of automated visual evaluation of cervical images taken with a smartphone camera. Int J Cancer . 2020 Apr 30
Sornapudi S, Brown G, Xue Z, Long LR, Allen L, Antani SK. Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image. AMIA Annu Symp Proc. 2019; 2019: 820–827.
Guo P, Xue Z, Long LR, Antani SK. 1. Anatomical landmark segmentation in uterine cervix images using deep learning. Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131810 (2 March 2020)
Zou J, Xue Z, Brown G, Long LR, Antani SK. Deep learning for nuclei segmentation and cell classification in cervical liquid based cytology. Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131811 (2 March 2020); doi: 10.1117/12.2549547
Guo P, Xue Z, Long LR, Antani SK. Cross-Dataset Evaluation of Deep Learning Networks for Uterine Cervix Segmentation. Diagnostics (Basel). 2020 Jan 14;10(1). pii: E44. doi: 10.3390/diagnostics10010044.
Rajaraman S, Candemir S, Xue Z, Alderson P, Thoma G, Antani SK. A Novel Stacked Model Ensemble for Improved TB Detection in Chest Radiographs. In Santosh KC et al. (Eds.). Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. (pp. 1-26). New York, NY: CRC Press, Taylor & Francis Group.
Xue Y, Zhou Q, Ye J, Long LR, Antani SK, Cornwell C, Xue Z, Huang X. Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification. ArXiv, abs/1907.10655.
Ganesan P, Xue Z, Singh S, Long LR, Ghoraani B, Antani SK. Performance Evaluation of a Generative Adversarial Network for Deblurring Mobile-phone Cervical Images. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 4487 – 4490.
Guo P, Singh S, Xue Z, Long LR, Antani SK. Deep Learning for Assessing Image Focus for Automated Cervical Cancer Screening. 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) DOI: 10.1109/BHI.2019.8834495.

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