Yuan Zhang

Yuan Zhang

Higher Degree by Research Candidate

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Yuan Zhang is a highly motivated and experienced researcher with a background in data science, chemical engineering, and bioengineering. Currently, Yuan is a PhD candidate at the University of Adelaide, where she is building an AI system to assist doctors with the diagnosis of endometriosis as part of the IMAGENDO project (https://imagendo.org.au/).

In Yuan's previous roles, she has gained experience in AI research, detecting harmful algal blooms and other pathogens, evaluating environmental pollution, and teaching. Yuan holds a Master's degree in Data Science from the University of Adelaide, where she received the Executive Dean's Recognition of Academic Excellence for her study. During Yuan's Master's degree, she designed and implemented a user interface for an AI-enabled colonoscopy system and constructed deep learning models to enrich its functions. Yuan has also completed a Master's degree in Chemical Engineering and a Bachelor's degree in Bioengineering.

Yuan is highly skilled in data analysis and machine learning. She is committed to using her skills and knowledge to contribute to the development of innovative solutions in the field of data science and AI.

As a PhD candidate at the University of Adelaide, Yuan's research focuses on developing an AI system to assist doctors in the diagnosis of endometriosis. This is a crucial component of the IMAGENDO project, which aims to improve the diagnosis and management of this condition.

Yuan's research involves the development of deep learning models that can accurately identify and classify endometriosis lesions in medical images, such as ultrasound and MRI scans.  This research has the potential to significantly improve the diagnosis and treatment of endometriosis, a condition that affects millions of women worldwide. By leveraging the power of AI and deep learning, Yuan's work is at the forefront of the field of medical imaging and has important implications for women's health.

Yuan's recent work "Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images" won the Best Oral Presentation Award at the 20th IEEE-ISBI 2023 International Symposium on Biomedical Imaging.

  • Appointments
    Date Position Institution name
    2017 - 2018 Research Scholar Oak Ridge Institute for Science and Education
    2017 - 2017 Research Scholar US Environmental Protection Agency (EPA)

  • Awards and Achievements
    Date Type Title Institution Name Country Amount
    2023 Award Best Oral Presentation Award in the 20th IEEE-ISBI 2023 International Symposium on Biomedical Imaging The 20th IEEE-ISBI 2023 International Symposium on Biomedical Imaging Colombia -

  • Language Competencies
    Language Competency
    Chinese (Mandarin) Can read, write, speak, understand spoken and peer review
    English Can read, write, speak, understand spoken and peer review

  • Education
    Date Institution name Country Title
    2021 - 2024 University of Adelaide Australia PhD of Computer Science
    2019 - 2021 University of Adelaide Australia Master of Data Science
    2014 - 2017 Southwest University of Science and Technology China Master of Chemical Engineering
    2010 - 2014 Southwest University of Science and Technology China Bachelor of Bioengineering

  • Research Interests
    Artificial Intelligence Medical and Health Sciences

  • Journals
    Year Citation
    2024 Avery, J. C., Deslandes, A., Freger, S. M., Leonardi, M., Lo, G., Carneiro, G., . . . Jenkins, M. (2024). Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence. Fertility and Sterility, 121(2), 164-188.
    DOI Scopus4 WoS2
    2024 Avery, J. C., Knox, S., Deslandes, A., Leonardi, M., Lo, G., Wang, H., . . . Jenkins, M. (2024). Noninvasive diagnostic imaging for endometriosis part 2: a systematic review of recent developments in magnetic resonance imaging, nuclear medicine and computed tomography. Fertility and Sterility, 121(2), 189-211.
    DOI Scopus1 WoS1

  • Conference Papers
    Year Citation
    2023 Hull, M. L., Wang, H., Zhang, Y., Avery, J., To, M. S., Carneiro, G., & Butler, D. (2023). The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification.. In Proceedings of the 45th IEEE Engineering in Medicine and Biology Society Vol. 2023 (pp. 5 pages). Online: IEEE.
    DOI
    2023 Butler, D., Wang, H., Zhang, Y., To, M. S., Avery, J. C., Hull, M. L., & Carneiro, G. (2023). The Effectiveness of Self-supervised Pre-training for Multi-modal Endometriosis Classification. In In Press. Sydney.
    2023 Zhang, Y., Wang, H., Avery, J. C., & Hull, M. L. (2023). Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) Vol. 2023-April (pp. 1-5). Cartagena de Indias Colombia: IEEE.
    DOI Scopus1
    2023 Wang, H., Ma, C., Zhang, J., Zhang, Y., Avery, J., Hull, L., & Carneiro, G. (2023). Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modality. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14223 LNCS (pp. 216-226). CANADA, Vancouver: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI
    2022 Butler, D., Zhang, Y., Chen, T., Shin, S. H., Singh, R., & Carneiro, G. (2022). In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos. In Proceedings of the 19th IEEE International Symposium on Biomedical Imaging (ISBI 2022)) Vol. 2022 (pp. 5 pages). Online: IEEE.
    DOI

I am currently tutoring the foundation of computer science course.

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