Dr Dongang Wang
Faculty of Medicine and Health
Dongang Wang is a research fellow at the Brain and Mind Centre in the University of Sydney. He joined BMC in 2019, and was awarded his PhD in Medicine and Health in 2024, with a specialization in Neuroimaging Analysis using Artificial Intelligence. Throughout his doctoral research, Dongang significantly contributed to the application of deep learning algorithms in clinical settings, tackling issues such as weakly-labelled data, noisy annotations, limited data sets, and the transition of laboratory research to FDA/TGA-approved medical devices.
Dongang Wang’s research interests center on integrating cutting-edge artificial intelligence (AI) algorithms into clinical workflows to enhance the analysis of medical images, with a particular emphasis on neuroimaging modalities like CT and MRI scans. His work extends beyond traditional qualitative diagnostics and quantitative semantic segmentation for brain-related diseases, and he is actively involved in adapting deep learning algorithms to address the complexities of real-world clinical environments across multiple medical centres. This includes tackling challenges such as limited annotations and data heterogeneity within federated learning frameworks. Moreover, Dongang is dedicated to leveraging large pre-trained models to improve performance and explainability in downstream tasks, ensuring that AI applications in medical imaging are both effective and transparent.
Publications
Journals
- Barnett, M., Wang, D., Beadnall, H., Bischof, A., Brunacci, D., Butzkueven, H., Brown, J., Cabezas Grebol, M., Das, T., Dugal, T., Klistorner, A., Kyle, K., Tang, Z., Zhan, G., Wang, C., et al (2023). A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis. npj Digital Medicine, 6(1). [More Information]
- Zhan, G., Wang, D., Cabezas Grebol, M., Bai, L., Kyle, K., Ouyang, W., Barnett, M., Wang, C. (2023). Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation. Frontiers in Neuroscience, 17, 1196087. [More Information]
- Wang, D., Jin, R., Shieh, C., Ng, A., Pham, H., Dugal, T., Barnett, M., Winoto, L., Wang, C., Barnett, Y. (2023). Real world validation of an AI-based CT hemorrhage detection tool. Frontiers in Neurology, 14. [More Information]
Conferences
- Wang, D., Wang, C., Masters, L., Barnett, M. (2020). Masked Multi-Task Network for Case-Level Intracranial Hemorrhage Classification in Brain CT Volumes. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Cham: Springer. [More Information]
- Wang, D., Ouyang, W., Li, W., Xu, D. (2018). Dividing and aggregating network for multi-view action recognition. 15th European Conference on Computer Vision ECCV2018, Cham: Springer. [More Information]
2023
- Barnett, M., Wang, D., Beadnall, H., Bischof, A., Brunacci, D., Butzkueven, H., Brown, J., Cabezas Grebol, M., Das, T., Dugal, T., Klistorner, A., Kyle, K., Tang, Z., Zhan, G., Wang, C., et al (2023). A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis. npj Digital Medicine, 6(1). [More Information]
- Zhan, G., Wang, D., Cabezas Grebol, M., Bai, L., Kyle, K., Ouyang, W., Barnett, M., Wang, C. (2023). Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation. Frontiers in Neuroscience, 17, 1196087. [More Information]
- Wang, D., Jin, R., Shieh, C., Ng, A., Pham, H., Dugal, T., Barnett, M., Winoto, L., Wang, C., Barnett, Y. (2023). Real world validation of an AI-based CT hemorrhage detection tool. Frontiers in Neurology, 14. [More Information]
2020
- Wang, D., Wang, C., Masters, L., Barnett, M. (2020). Masked Multi-Task Network for Case-Level Intracranial Hemorrhage Classification in Brain CT Volumes. 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Cham: Springer. [More Information]
2018
- Wang, D., Ouyang, W., Li, W., Xu, D. (2018). Dividing and aggregating network for multi-view action recognition. 15th European Conference on Computer Vision ECCV2018, Cham: Springer. [More Information]