Dr. xiaoqi sheng | Biological information processing | Best Researcher Award

Assistant professor, South China University of Technology China

Xiaoqi Sheng is a Ph.D. candidate in Computer Science and Technology at South China University of Technology, advised by Dr. Hongmin Cai. His research specializes in applying Artificial Intelligence (AI) to neuroscience, brain disease modeling, biomedical data analytics, and medical image segmentation. Sheng has published extensively in high-impact journals such as IEEE Transactions on Neural Networks and Learning Systems. He has contributed significantly to Alzheimer’s disease detection and brain network classification using advanced machine learning models. Sheng has collaborated internationally, including research exchanges at Soochow University and online collaborations with the University of Texas at Arlington. His work focuses on AI-driven insights into brain function and medical diagnostics. Recognized for academic excellence, he has received prestigious scholarships and awards. With a strong foundation in control engineering and electronic information engineering, Sheng continues to push the boundaries of AI applications in medical science and fundamental neuroscience.

Profile

Scopus

Scholar

🎓 Education 

Xiaoqi Sheng is currently pursuing a Ph.D. in Computer Science and Technology at South China University of Technology (2020-2024). He earned an M.S. in Control Engineering from Jiangxi University of Science and Technology (2017-2020) under Dr. Liming Liang and Dr. Xinjian Chen. His undergraduate studies in Electronic Information Engineering were completed at Liaocheng University Dongchang College (2013-2017). He also participated in academic exchanges at Soochow University (2018-2019), focusing on fundus image segmentation, and at The University of Texas at Arlington (2022-2024), specializing in imaging genomics analysis.

🏆 Experience 

Sheng has conducted extensive research on AI applications in neuroscience, working on brain disease modeling and fundamental brain organization principles. His collaborations include research on medical image segmentation and manifold learning techniques for diagnostic applications. During his exchange at Soochow University, he developed techniques for glycoconjugate network diagnostics. His collaboration with The University of Texas at Arlington involved imaging genomics analysis for brain disorders. His expertise spans artificial intelligence, biomedical data analytics, and discriminant analysis modeling, with a focus on enhancing medical diagnostic accuracy.

🏅 Awards and Honors 

Sheng has received numerous accolades, including the Wuliangye Scholarship for Outstanding Students (2023) and the National Scholarship from the Ministry of Education of China (2020). His master’s thesis was recognized as Excellent in Jiangxi Province (2023), and he was honored as an Outstanding Graduate at Jiangxi University of Science and Technology (2020). Additionally, he secured First Prize at the 15th Graduate Student Academic Forum of Jiangxi University of Science and Technology (2019), reflecting his contributions to AI-driven biomedical research.

🔬 Research Focus 

🔍Conclusion

Publication

• GA-Net: Ghost convolution adaptive fusion skin lesion segmentation network (2023) – L. Zhou, L. Liang, X. Sheng – 14 citations

 

• SGUNet: Style-guided UNet for adversely conditioned fundus image super-resolution (2021) – Z. Fan, T. Dan, B. Liu, X. Sheng, H. Yu, H. Cai – 10 citations

 

• U-shaped retinal vessel segmentation algorithm based on adaptive scale information (2019) – L. Liang, X. Sheng, Z. Lan, G. Yang, X. Chen – 10 citations

 

• Deep manifold harmonic network with dual attention for brain disorder classification (2022) – X. Sheng, J. Chen, Y. Liu, B. Hu, H. Cai – 7 citations

 

• Segmentation of retinal vessels by fusing contour information and conditional generative adversarial (2021) – L. Liang, Z. Lan, X. Sheng, Z. Xie, W. Liu – 4 citations

 

• Brain Network Classification for Accurate Detection of Alzheimer’s Disease via Manifold Harmonic Discriminant Analysis (2023) – H. Cai, X. Sheng, G. Wu, B. Hu, Y.M. Cheung, J. Chen – 2 citations

 

• Colorectal polyp segmentation method based on fusion of transformer and cross-level phase awareness (2023) – L. Liang, A. He, C. Zhu, X. Sheng – 2 citations

 

• Retinal vessel segmentation based on W-net conditional generative adversarial nets (2021) – L. Liang, Z. Lan, W. Xiong, X. Sheng – 2 citations

 

• Retinal vessels segmentation algorithm based on multi-scale filtering (2019) – S. Xiaoqi, Z. Lan – 2 citations

 

• Modality-aware discriminative fusion network for integrated analysis of brain imaging genomics (2024) – X. Sheng, H. Cai, Y. Nie, S. He, Y.M. Cheung, J. Chen – 1 citation

 

• A level set method with region-scalable fitting energy for retinal layer segmentation in spectral-domain optical coherence tomography images (2020) – L. Liang, X. Sheng, B. Liu, Z. Lan – 1 citation

 

• SFIT-Net: Spatial Reconstruction Feature Interaction Transformer Retinal Vessel Segmentation Algorithm (2025) – L. Liang, B. Lu, J. Wu, Y. Li, X. Sheng – No citations yet

 

• U-shaped Retinal Vessel Segmentation Based on Adaptive Aggregation of Feature Information (2022) – LLJFLZJYX Sheng – No citations yet

 

• Improved U-Net fundus retinal vessels segmentation (2020) – L. Liming, S. Xiaoqi, G. Kai – No citations yet