Prof. Alan Wang

Department of Anatomy and Medical Imaging

The University of Auckland, New Zealand

Associate Professor

Email:[email protected]

Qualifications

2006 Ph.D., the Hong Kong Polytechnic University, Hong Kong (China), Computer Science

Publications

1. Yu, J. F., Mdletshe, S., Abbasi, H., et al. (2026). Deep learning for brain MRI artifact correction: Current challenges and future directions. Bioengineering-Basel, 13(6), Article 699.
2. Pau, S., Eglinton, T., Wang, A. L., et al. (2026). The role of artificial intelligence in pre-operative prediction of completeness of cytoreduction for peritoneal surface malignancies: A scoping review. EJSO, 52(4), Article 111480.
3. Zhang, D., Li, Z. Y., Deng, X. F., et al. (2026). Asymmetric fiber orientation distribution estimation via unsupervised deep learning. Medical Image Analysis, 110, Article 103968.
4. Pau, S., Eglinton, T., Wang, A. L., et al. (2026). CT-based radiomics-clinical machine-learning model to predict completeness of cytoreduction in colorectal peritoneal metastases. Colorectal Disease, 28(2), Article e70409.
5. Narula, V., Taylor, D., Mclaren, R., et al. (2026). The impact of balance exercise on brain age and brain morphometry: Insights from MRI analysis. Aging Clinical and Experimental Research, 38(1), Article 71.
6. Peng, Z. L., Liu, J. J., Zhao, L. Y., et al. (2026). Neural network guided global least squares for quantitative biochemical component analysis in Raman spectroscopy. Analytical Chemistry, 98(3), 1928–1938.
7. Khan, A., Shim, V., Fernandez, J., et al. (2025). Review of deep learning models with Spiking Neural Networks for modeling and analysis of multimodal neuroimaging data. Frontiers in Neuroscience, 19, Article 1623497.
8. Pau, S. S. S., Eglinton, T., Wang, A. L., et al. (2025). Pre-operative predictors of completeness of cytoreduction for colorectal peritoneal metastases: A systematic review of radiological, clinicopathological and biomarkerbased predictors. Asia-Pacific Journal of Clinical Oncology, 21(S5).
9. Pau, S., Eglinton, T., Wang, A. L., et al. (2025). Pre-operative predictors of completeness of cytoreduction for colorectal peritoneal metastases: A systematic review of radiological, clinicopathological and biomarker-based predictors. EJSO, 51(12), Article 110537.
10. Sasidharan, S. M., Mdletshe, S., & Wang, A. L. (2025). Machine learning in stroke lesion segmentation and recovery forecasting: A review. Applied Sciences-Basel, 15(18), Article 10082.
11. Saludar, C. J., Tayebi, M., Kwon, E., et al. (2025). Effect of subconcussive head impacts on the white matter microstructure of contact sports athletes. Brain Injury, 39(S1), S218–S219.
12. Lu, Y. B., & Wang, A. L. (2025). Integrating language into medical visual recognition and reasoning: A survey. Medical Image Analysis, 102, Article 103514.
13. Karnadipa, T., Chong, B. J. M., Shim, V., et al. (2024). Mapping stroke outcomes: A review of brain connectivity atlases. Journal of Neuroimaging, 34(5), 548–561.
14. Usategui, I., Arroyo, Y., Torres, A. M., et al. (2024). Systemic lupus erythematosus: How machine learning can help distinguish between infections and flares. Bioengineering-Basel, 11(1), Article 90.
15. Esmaeili, F., Cassie, E., Nguyen, H. P. T., et al. (2023). Utilizing deep learning algorithms for signal processing in electrochemical biosensors: From data augmentation to detection and quantification of chemicals of interest. Bioengineering-Basel, 10(12), Article 1348.
16. Kasabov, N. K., Bahrami, H., Doborjeh, M., et al. (2023). Brain-inspired spatio-temporal associative memories for neuroimaging data classification: EEG and fMRI. Bioengineering-Basel, 10(12), Article 1341.
17. Mcnaughton, J., Fernandez, J., Holdsworth, S., et al. (2023). Machine learning for medical image translation: A systematic review. Bioengineering-Basel, 10(9), Article 1078.
18. Kocak, B., Chepelev, L. L., Chu, L. C., et al. (2023). Assessment of RadiomIcS rEsearch (ARISE): A brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology. European Radiology, 33(11), 7556–7560.
19. Yoo, J. H., Chong, B. J. M., Barber, P. A., et al. (2023). Predicting motor outcomes using atlas-based voxel features of post-stroke neuroimaging: A scoping review. Neurorehabilitation and Neural Repair, 37(7), 475–487.
20. Esmaeili, F., Cassie, E., Nguyen, H. P. T., et al. (2023). Anomaly detection for sensor signals utilizing deep learning autoencoder-based neural networks. Bioengineering-Basel, 10(4), Article 405.

Profile Details

https://profiles.auckland.ac.nz/alan-wang
https://scholar.google.com/citations?user=BS58mgUAAAAJ&hl=zh-TW
https://www.researchgate.net/scientific-contributions/Alan-Wang-2164685545

WOS ResearcherID: MUE-4960-2025