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:

Liang Z, Xue Z, Rajaraman S, Antani SK. Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation. New Microbes and New Infections, Volume 62, 2024, 101457, ISSN 2052-2975, https://doi.org/10.1016/j.nmni.2024.101457.

Liang Z, Xue Z, Rajaraman S, Antani S. Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation. New Microbes and New Infections. New Microbes and New Infections, Volume 62, 2024, 101457, ISSN 2052-2975, https://doi.org/10.1016/j.nmni.2024.101457.

Rajaraman S, Liang Z, Xue Z, Antani S. Noise-Induced Modality-Specific Pretext Learning for Pediatric Chest X-ray Image Classification. Frontiers in Artificial Intelligence, vol. 7, 2024, doi: 10.3389/frai.2024.1419638, ISSN: 2624-8212.

Rajaraman S, Zamzmi G, Yang F, Liang Z, Xue Z, Antani SK. Semantically redundant training data removal and deep model classification performance: A study with chest X-rays. Computerized Medical Imaging and Graphics. Volume 115, 2024, 102379, ISSN 0895-6111, https://doi.org/10.1016/j.compmedimag.2024.102379.

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