PHD at Nanjing University of Aeronautics and Astronautics, China📖
Chaojun Li is a Master’s student in Computer Technology at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (2022–2025). He holds a Bachelor’s degree in Material Forming and Control Engineering from the School of Advanced Manufacturing, Nanchang University (2018–2022). His research focuses on deep learning, medical image analysis, and graph neural networks, with a strong publication record in high-impact journals and international conferences. Chaojun has received numerous awards, including the Best Paper Award at PRMVIA 2024 and multiple scholarships for academic excellence.
Profile
Scopus Profile
Education Background🎓
- Master of Engineering, Computer Technology
Nanjing University of Aeronautics and Astronautics (2022–2025)
- Bachelor of Engineering, Material Forming and Control Engineering
Nanchang University (2018–2022)
Professional Experience🌱
Chaojun Li has made significant contributions to the field of medical image analysis and brain disease diagnosis using advanced deep learning techniques. He has authored multiple high-impact research papers, including publications in top-tier journals such as NeuroImage and IEEE Transactions on Medical Imaging. His research achievements have earned him recognition, including the Best Paper Award at PRMVIA 2024. In addition to his academic pursuits, Chaojun has actively contributed to patent development, ranking second in a published multimodal brain network classification patent and participating in three additional authorized or published patents. He has consistently demonstrated academic excellence through various awards and scholarships.
Chaojun Li’s research interests focus on deep learning, medical image analysis, and graph neural networks, specifically in the context of brain disease diagnosis. His work leverages advanced techniques such as spatio-temporal graph attention networks, multi-modal fusion models, and hypergraph transformer networks to improve diagnostic accuracy and early detection of brain diseases. By integrating multimodal data and using cutting-edge neural network architectures, he aims to contribute to the development of more effective diagnostic tools in the medical field, enhancing patient outcomes and facilitating better clinical decision-making. Chaojun’s research not only addresses challenges in computational neuroscience but also explores practical applications of machine learning and artificial intelligence in healthcare.
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Publications Top Notes 📄
1. Multi-View Graph Attention Complementary based Brain Networks Analysis for Brain Diseases Diagnosis
- Authors: Li, C., Li, S., Zhu, Q.
- Conference: Proceedings – 2024 2nd International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA 2024)
- Year: 2024
- Pages: 22–27
- Award: Best Paper Award
- Abstract: This paper presents a novel approach for brain disease diagnosis through multi-view graph attention networks. The method leverages complementary information from multiple brain network views to improve diagnostic accuracy. By utilizing graph attention mechanisms, the study enhances the ability to model the intricate relationships in brain data, resulting in improved diagnosis of brain diseases. This method is expected to significantly advance brain disease diagnostic tools, offering a more precise and efficient model for clinical use.
2. Multi-Kernel Learning based Disease Diagnosis with Multi-Atlas
- Authors: Yao, Y., Li, C.
- Conference: Proceedings – 2023 7th International Symposium on Computer Science and Intelligent Control (ISCSIC 2023)
- Year: 2023
- Pages: 176–182
- Abstract: This paper introduces a multi-kernel learning approach for disease diagnosis using multi-atlas image segmentation. The proposed model integrates multiple kernels to capture diverse data features, enhancing diagnostic performance across various disease types. The multi-atlas strategy improves the robustness of the model by incorporating a broad range of anatomical information, aiding in the accurate diagnosis of medical conditions. This approach demonstrates the utility of kernel-based learning for effective disease classification and highlights its application in medical image analysis.
Conclusion
Dr. Chaojun Li is a highly deserving candidate for the Best Researcher Award, given his significant contributions to deep learning and medical image analysis. His innovative approaches in brain disease diagnosis, strong academic performance, and recognition at international platforms highlight his excellence in research.
However, to further enhance the impact of his work, expanding collaborations, broadening his research scope, and increasing interdisciplinary engagement would strengthen his contributions to both academic and clinical settings. Dr. Li is poised to make significant future advancements in healthcare technology, and with continued growth in these areas, he can elevate his work even further to benefit both scientific and medical communities globally.