Shuying Li, Ph.D.

Education

• Ph.D., Biomedical Engineering, Washington University in St. Louis, 2023
• M.S., Mechanical Engineering, University of Michigan, 2018
• B.S., Energy and Environmental Systems Engineering, Zhejiang University, 2016

Research Interests

• AI for biomedical imaging
• Neurodegenerative diseases
• Multi-modal AI / Physics-informed AI / Explainable AI
• Foundation model

Research Bio

I completed my Ph.D. in Biomedical Engineering at Washington University in St. Louis in 2023 and postdoctoral training at Boston University in 2025. My research develops AI tools for biomedical imaging to improve diagnosis, deepen biological insight, and support clinical decision-making.

Courses Taught

• CPB 421 Bioethics
• CPB 328 Bioinstrumentation

Publications

As First Author

• Li, S., Malamut, M., McKee, A., Cherry, J.*, & Tian, L.* Age-Informed, Attention-Based Weakly-Supervised Learning for
  Neuropathological Image Assessment. bioRxiv (2025): 2025-05.
• Li, S., Zhang, M., Xue, M., & Zhu, Q. (2024). Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment. Journal of Biophotonics, 202300483.
• Zhang, M.†, Li, S. †, Xue, M., & Zhu, Q. (2023), A two-step classification scheme for breast lesion diagnosis using ultrasound-guided diffuse optical tomography and deep learning. Journal of Biomedical Optics, 28(8), 086002-086002.
• Li, S.†, Luo, H.†, Kou, S., Hagemann, I., & Zhu, Q. (2023). Depth-resolved attenuation mapping of human ovary and fallopian tube using optical coherence tomography. Journal of Biophotonics, e202300002.
• Li, S., Zhang, M., Xue, M., & Zhu, Q. (2022). Difference imaging from single measurements in diffuse optical tomography: A deep learning approach. Journal of Biomedical Optics, 27(8), 086003. (Promoted on the SPIE WeChat account)
• Luo, H.†, Li, S.†, Zeng, Y.†, …, Zhu, Q. (2022). Human colorectal cancer tissue assessment using optical coherence tomography catheter and deep learning. Journal of Biophotonics, e202100349. (Inside cover, top-10 most cited papers among papers published in 2022 in Journal of Biophotonics)
  WashU The Source: https://source.washu.edu/2022/08/pairing-imagin...
• Li, S., Zhang, M., & Zhu, Q. (2021). Ultrasound segmentation-guided edge artifact reduction in diffuse optical tomography using connected component analysis. Biomedical Optics Express, 12(8), 5320-5336.
• Li, S., Huang, K., Zhang, M., Uddin, K. S., & Zhu, Q. (2021). Effect and correction of optode coupling errors in breast imaging using diffuse optical tomography. Biomedical Optics Express, 12(2), 689-704. (Most downloaded papers)
• Li, S.†, Zeng, Y.†, Chapman Jr, W. C. †, Erfanzadeh, M., Nandy, S., Mutch, M., & Zhu, Q. (2020). Adaptive Boosting (AdaBoost)‐based multiwavelength spatial frequency domain imaging and characterization for ex vivo human colorectal tissue assessment. Journal of Biophotonics, 13(6), e201960241. (Inside cover)
  WashU School of Engineering News & Events: https://engineering.wustl.edu/news/2020/Machine...
• Li, S. †, Qin, Y. †, Wang, X., & Yang, X. (2018). Bubble growth in cylindrically-shaped optical absorbers during photo-mediated ultrasound therapy. Physics in Medicine & Biology, 63(12), 125017.

As a Contributing Author

• Luo, H., Nie, H., Lamm, V., Li, S., Thakur, S., Hollander, T., Cho, D., …, & Zhu, Q. In vivo Evaluation of Complex Polyps with Endoscopic Optical Coherence Tomography and Deep Learning during Routine Colonoscopy: A Feasibility Study. (Accepted, Scientific Reports)
• Xue, M., Li, S., & Zhu, Q. (2024). Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model. Journal of Biomedical Optics, 29(8), 086001-086001.
• Xue, M., Zhang, M., Li, S., Zou, Y., & Zhu, Q. (2023). Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography. Biomedical Optics Express, 14(11), 6072-6087.
• Luo, H., Li, S., Kou, S., Lin, Y., Hagemann, I. S., & Zhu, Q. (2023). Enhanced 3D visualization of human fallopian tube morphology using a miniature optical coherence tomography catheter. Biomedical Optics Express, 14(7), 3225-3233.
• Zhang, M., Xue, M., Li, S., Zou, Y., & Zhu, Q. (2023). Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification. Biomedical Optics Express, 14(4), 1636-1646.
• Poplack, S. P., Young, C. A., Hagemann, I. S., Luo, J., Herman, C. R., Wiele, K., Li, S., ... & Zhu, Q. (2021). Prospective assessment of adjunctive ultrasound-guided diffuse optical tomography in women undergoing breast biopsy: Impact on BI-RADS assessments. European journal of radiology, 145, 110029.
• Zhang, M., Li, S., Zou, Y., & Zhu, Q. (2021). Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall. Journal of Biomedical Optics 26 (10), 106004
• Zou, Y., Zeng, Y., Li, S., & Zhu, Q. (2021). Machine learning model with physical constraints for diffuse optical tomography. Biomedical Optics Express, 12(9), 5720-5735. 
• Zeng, Y., Chapman Jr, W. C., Lin, Y., Li, S., Mutch, M., & Zhu, Q. (2021). Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography. Journal of biophotonics, 14(1), e202000276.
• Zhang, M., Uddin, K. S., Li, S., & Zhu, Q. (2020). Target depth-regularized reconstruction in diffuse optical tomography using ultrasound segmentation as prior information. Biomedical Optics Express, 11(6), 3331-3345.
• Samanta, S., Jiang, J., Ye, D., Hutchinson, M., Zeng, Y., Li, S., ... & Tai, Y. C. (2020). Point-of-Care PET/Ultrasound Imaging for Detection of Atherosclerotic Vulnerable Plaque: A Feasibility Study. Journal of nuclear medicine 61 (supplement 1), 310-310.
• Zeng, Y., Xu, S., Chapman, W. C., Li, S., Alipour, Z., Abdelal, H., & Chatterjee, D. (2020). Real-time colorectal cancer diagnosis using PR-OCT with deep learning. Theranostics, 10(6), 2587.
• Zeng, Y., Nandy, S., Rao, B., Li, S., Hagemann, A. R., Kuroki, L. K., ... & Zhu, Q. (2019). Histogram analysis of en face scattering coefficient map predicts malignancy in human ovarian tissue. Journal of Biophotonics, 12(11), e201900115.