Yuan Zhang
Adelaide Medical School
Faculty of Health and Medical Sciences
Yuan Zhang is a Grant-Funded Researcher specializing in integrating Artificial Intelligence (AI) into medical diagnostics, with a focus on women’s reproductive health. Her research aims to address critical challenges in diagnostic delays and accessibility, particularly for conditions such as endometriosis.
Yuan is a core contributor to the award-winning IMAGENDO® project (https://imagendo.org.au/), which received the Eureka Prize for Innovative Use of Technology in 2023. This groundbreaking initiative focuses on developing non-invasive, AI-driven tools to transform the diagnosis of endometriosis. Within IMAGENDO®, Yuan has played a pivotal role in designing and optimizing AI models that integrate MRI and transvaginal ultrasound (TVUS) data, overcoming key technical challenges such as unpaired datasets while ensuring the solutions are clinically relevant.
With a multidisciplinary background in bioengineering, chemical engineering, data science, and computer science, Yuan brings a unique perspective to tackling complex medical challenges. She is a member of the Robinson Research Institute (RRI) and the Australian Institute for Machine Learning (AIML). She combines advanced AI methodologies with clinical insights through these affiliations, ensuring her work bridges technical innovation and practical healthcare applications.
Yuan’s contributions have resulted in high-impact publications, recognition at international conferences, and a provisional patent for an AI-based diagnostic tool. Her research continues to advance the integration of AI in women’s health diagnostics, driving innovation and improving patient outcomes.
Yuan Zhang’s research focuses on leveraging Artificial Intelligence (AI) to revolutionize medical diagnostics, with a particular emphasis on improving outcomes in women’s reproductive health. Her work addresses critical challenges such as diagnostic delays and the need for non-invasive solutions, particularly for conditions like endometriosis.
As a key contributor to the award-winning IMAGENDO® project, Yuan has developed cutting-edge AI models that integrate unpaired MRI and transvaginal ultrasound (TVUS) data. Her innovative approach addresses significant challenges in medical imaging, making diagnostic tools more accurate and scalable to clinical practice. Her achievements include high-impact publications, a provisional patent, and recognition at international conferences, including the Best Oral Award at ISBI 2023.
Yuan’s contributions have been recognized with the Lloyd Cox O&G Research Fund – 2025 People Support Scheme, which supports her efforts to optimize and validate AI-driven diagnostic tools. She is a member of the AIML Ambassador Program, where she fosters interdisciplinary collaborations, and the Robinson Research Institute, where she works closely with clinicians to ensure her solutions meet real-world needs.
By collaborating with sonographers, radiologists, and gynecologists, Yuan bridges the gap between technical innovation and clinical application. Her work is poised to transform diagnostic practices, reduce delays, and improve accessibility in women’s healthcare.
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Appointments
Date Position Institution name 2024 - ongoing Grant-Funded Researcher University of Adelaide 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
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Journals
Year Citation 2025 Wang, H., Butler, D., Zhang, Y., Avery, J., Knox, S., Ma, C., . . . Carneiro, G. (2025). Human-AI collaborative multi-modal multi-rater learning for endometriosis diagnosis. PHYSICS IN MEDICINE AND BIOLOGY, 70(1), 13 pages.
2024 Avery, J. C., Deslandes, A., Freger, S. M., Leonardi, M., Lo, G., Carneiro, G., . . . Imagendo Study Group. (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.
Scopus10 WoS2 Europe PMC52024 Avery, J. C., Knox, S., Deslandes, A., Leonardi, M., Lo, G., Wang, H., . . . Imagendo Study Group. (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.
Scopus5 WoS1 Europe PMC1 -
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 Scopus12023 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 Proceedings of 45th IEEE Engineering in Medicine and Biology Society. Online: IEEE.
DOI2023 Zhang, Y., Wang, H., Butler, D., To, M. -S., Avery, J. C., Hull, M. L., & Carneiro, G. (2023). Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images. In Proceedings of the IEEE 20th International Symposium on Biomedical Imaging (ISBI 2023) Vol. 2023-April (pp. 1-5). Cartagena de Indias, Colombia: IEEE.
DOI Scopus22023 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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14223 (pp. 216-226). Vancouver, BC, Canada: Springer Nature Switzerland.
DOI Scopus82022 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 Scopus2
- Lloyd Cox O&G Research Fund – 2025 People Support Scheme
- Adelaide Graduate Research School Travel Grant (2023)
- Robinson Research Institute Travel Grant (2023)
Tutor | The University of Adelaide (Feb 2023 – Jul 2023)： Supported undergraduate students in Python programming courses, assisting with tutorials, assignments, and assessments.
Research Collaboration： Worked with Honours and PhD researchers on projects, resulting in joint publications and shared research outcomes
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