PERSONNEL

Stanley Liang photo

Stanley Liang, PhD

Computational Health Research Branch

Contact InformationNihbc 38A - Lister Hill 1003l 301.496.8837
zhaohui.liang@nih.gov


Expertise and Research Interests:

Dr. Stanley Liang is a postdoctoral research fellow in the Computational Health Research Branch, National Library of Medicine, NIH. He received his PhD degree in Computer Science from York University, Canada in 2022. He also received his Doctor of Medicine from Guangzhou University of Chinese Medicine, China, and Master of Public Health (MPH) from Sun Yat-sen University, China. Throughout his academic career so far, Dr. Liang has published 26 research papers, including 15 papers as first author in peer review journals and IEEE conference proceedings. The research interest of Dr. Liang includes deep learning for medical image processing, generative learning for medical image synthesis with generative adversarial network (GAN), and natural language processing (NLP) for electronic health records, and medical genomics.

Publications:

Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani SK. Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images. PLOS Digital Health 3(1): e0000286. https://doi.org/10.1371/journal.pdig.0000286.

Liang Z, Xue Z, Feng Y, Rajaraman S, Huang JX, Antani SK. Emergency Department Wait Time Forecast based on Semantic and Time Series Patterns in COVID-19 Pandemic. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 3067-3072, doi: 10.1109/BIBM58861.2023.10385758.

Liang Z, Xue Z, Rajaraman S, Feng Y, Antani SK. Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning. In: Xue Z, et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_12.

Liang Z, Huang JX, Antani SK. Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN. Sensors (Basel). 2022 Dec 8;22(24):9628. DOI: 10.3390/s22249628.

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