Dr. Yu Zhou is an emerging researcher in the intersecting domains of medical imaging, neuroscience, and artificial intelligence, recognized for advancing computational approaches that improve the understanding and diagnosis of neurological disorders. With 10 published research documents, 98 citations, an h-index of 7, and an i10-index of 6, his scholarly contributions reflect both productivity and growing international influence. His research has led to notable advancements in diffusion MRI analysis, white-matter connectivity modeling, and machine-learning-driven diagnostic frameworks, particularly within mild cognitive impairment (MCI), juvenile myoclonic epilepsy (JME), and neurobehavioral disorders.Yu Zhou’s most cited works demonstrate strong expertise in fiber-specific white matter analysis, CNN-based transfer learning, and automated classification systems, with contributions published in respected venues such as Cerebral Cortex, Frontiers in Aging Neuroscience, Frontiers in Neuroscience, and Journal of Neural Engineering. His research extends beyond human neuroscience to impactful cross-disciplinary applications, including AI-driven acoustic-based detection systems for livestock estrus identification, showcasing versatility and methodological depth.He has served as principal investigator for two provincial projects, participated in four additional provincial projects and one national project, and contributed to one consultancy/industry initiative, indicating growing leadership in funded research. His innovative capabilities are further evidenced by one granted patent and four patents under review, underscoring his commitment to translational and societally relevant technological development. With collaborations established across computational neuroscience and AI imaging research groups, he continues to contribute to global scientific networks.Yu Zhou’s ongoing work focuses on building interpretable deep-learning models, advancing multimodal data fusion for clinical diagnostics, and developing AI-assisted neuroimaging biomarkers for early disease identification. These contributions hold significant promise for clinical decision support, early-stage neurological assessment, and precision medicine applications. With increasing publication momentum and expanding collaborative research engagements, he is positioned to generate deeper scientific impact and contribute to the evolution of intelligent medical imaging and computational neuroscience.
Featured Publications
1.Zhou, Y., Si, X., Chen, Y., Chao, Y., Lin, C. P., Li, S., Zhang, X., Ming, D., & Li, Q. (2022). Hippocampus- and thalamus-related fiber-specific white matter reductions in mild cognitive impairment. Cerebral Cortex, 32(15), 3159–3174. Cited By : 23
2.Si, X., Zhang, X., Zhou, Y., Sun, Y., Jin, W., Yin, S., Zhao, X., Li, Q., & Ming, D. (2020). Automated detection of juvenile myoclonic epilepsy using CNN-based transfer learning in diffusion MRI. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. Cited By : 18
3.Zhou, Y., Si, X., Chao, Y. P., Chen, Y., Lin, C. P., Li, S., Zhang, X., Sun, Y., & Ming, D. (2022). Automated classification of mild cognitive impairment by machine learning with hippocampus-related white matter network. Frontiers in Aging Neuroscience, 14, 866230.Cited By : 13
4.Wang, J., Si, Y., Wang, J., Li, X., Zhao, K., Liu, B., & Zhou, Y. (2023). Discrimination strategy using machine learning technique for oestrus detection in dairy cows by a dual-channel-based acoustic tag. Computers and Electronics in Agriculture, 210, 107949.Cited By : 13
5.Wang, J., Chen, H., Wang, J., Zhao, K., Li, X., Liu, B., & Zhou, Y. (2023). Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag. animal, 17(6), 100811.Cited By : 12