Dr. Tongtong Che | Brain Image Analysis – Postdoctor at Beijing Normal University, China.
Dr. Tongtong Che is a postdoctoral researcher specializing in medical image analysis, with a robust background in deep learning and medical imaging registration techniques. Born on June 24, 1995, she currently works at the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University. With extensive training in biological science, medical engineering, and information science, Dr. Che has developed innovative algorithms for brain image registration, an area crucial for neuroimaging studies and disease diagnostics. She has published extensively in top-tier journals such as Medical Image Analysis and IEEE Transactions on Image Processing, with her work widely recognized for its technical sophistication and clinical relevance. In addition to academic accomplishments, she gained industry experience through a research internship at Shanghai United Imaging Intelligence. Dr. Che is also the recipient of the 2024 Best Researcher Award at the International Conference on Neurology and Neuro Disorders, highlighting her growing international impact. Her research continues to bridge the gap between advanced computational methodologies and clinical applications in neuroscience and medical imaging. Dr. Che’s ultimate aim is to contribute to the development of intelligent systems that enhance healthcare diagnostics and treatment planning.
Publication Profile
Scopus
Educational Details
Dr. Tongtong Che holds a Doctor of Engineering degree in Biological Science and Medical Engineering from Beihang University, awarded in June 2024. During her doctoral studies, she focused on medical image processing, particularly brain image registration techniques using deep learning frameworks. Prior to this, she earned a Master of Science degree from the School of Information Science and Engineering at Shandong Normal University in 2020. Her master’s work involved image processing and algorithmic optimization, laying the foundation for her later work in medical imaging. Dr. Che began her academic journey at Shandong Women’s University, where she obtained a Bachelor of Science in Information Science and Technology in 2017. Across all her academic stages, she consistently demonstrated excellence in computational modeling, medical data analysis, and interdisciplinary collaboration. Her educational path reflects a progressive deepening of expertise, from general information technology to highly specialized medical image analysis, aligning her with the forefront of intelligent healthcare research. This solid educational background empowers her to address complex challenges in brain mapping and deformable image registration, particularly for pediatric and neurological conditions.
Professional Experience
Dr. Tongtong Che currently serves as a postdoctoral researcher at the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University, a role she has held since September 2020. In this capacity, she leads research on brain image registration—a foundational process for constructing accurate brain templates and understanding developmental brain changes. Her work involves developing deep learning algorithms for multi-spectral and 3D image registration. Prior to this, Dr. Che completed a research internship at Shanghai United Imaging Intelligence in 2019, where she optimized brain image registration algorithms in a high-tech industrial setting. These experiences have endowed her with both academic depth and practical proficiency in translating computational models to real-world applications. She has collaborated with leading experts in the field and co-authored multiple high-impact journal articles. Her professional portfolio demonstrates a rare balance between theoretical innovation and applied research, supported by strong interdisciplinary collaboration. Through her combined academic and industry experience, Dr. Che has emerged as a key contributor to advancing image analysis technologies that support clinical neuroscience and brain development research.
Research Interest
Dr. Tongtong Che’s research interests are centered on medical image processing and analysis, with particular expertise in brain image registration and deep learning-based methods for biomedical applications. She is passionate about developing computational tools that enable more accurate and efficient analysis of medical images, which are critical in diagnostic and prognostic procedures. Her primary research explores group-wise and deformable registration techniques using hierarchical and graph-based models. She also works on template construction and multilevel modeling for pediatric brain imaging, especially under complex deformations associated with growth and neurodevelopment. Dr. Che’s secondary interests include radiomics, image segmentation, and multimodal image fusion. Her interdisciplinary approach leverages artificial intelligence and neuroinformatics to uncover structural and functional patterns in medical images. Dr. Che aims to integrate machine learning with biomedical imaging to support precision medicine, early disease detection, and patient-specific treatment planning. With the increasing availability of medical data and advances in AI, she is committed to pushing the boundaries of image-guided healthcare technologies.
Research Skills
Dr. Tongtong Che possesses a broad and advanced set of research skills at the intersection of computer science and biomedical engineering. Her core technical expertise includes medical image registration, deep neural networks, generative adversarial networks (GANs), and optical flow estimation. She is proficient in implementing and optimizing complex image processing pipelines, particularly for multi-spectral and 3D brain MRI datasets. Dr. Che has extensive programming experience in Python and MATLAB, with a strong command of deep learning frameworks such as TensorFlow and PyTorch. She is skilled in statistical modeling, data visualization, and algorithm evaluation for reproducibility and robustness. Her methodological innovations include the development of AMNet and DGR-Net, two state-of-the-art architectures for deformable registration. In addition, she has successfully applied her skills in collaborative environments, including academic labs and industrial R&D settings. Dr. Che is also adept at preparing manuscripts for peer-reviewed journals and conference presentations, demonstrating a high level of scientific communication. Her research toolkit positions her well for future developments in image-based diagnostics and personalized medicine.
Awards and Honors
Dr. Tongtong Che has been recognized for her exceptional research contributions with the Best Researcher Award at the 5th Edition of the International Conference on Neurology and Neuro Disorders in 2024. This award acknowledges her innovative work in brain image registration and its implications for neurodevelopmental and neurological studies. In the same year, she secured funding from the China Postdoctoral Science Foundation under the prestigious Postdoctoral Innovation Talent Support Program (BX20240039). Her funded project focuses on constructing dynamic developmental brain maps for children aged 0 to 18 years with complex anatomical deformations. The grant, worth 640,000 yuan, supports her research from June 2024 to June 2026 and reflects national-level recognition of her scientific potential and leadership. These honors highlight Dr. Che’s impact and visibility in the medical imaging and neuroscience communities. She continues to advance her work at the cutting edge of biomedical engineering with an aim to improve both the scientific understanding of brain development and the clinical tools available for pediatric diagnosis.
Author Metrics
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Total Publications: 11
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Total Citations: 136
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h-index: 8
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g-index: 10
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i10-index: 5
Top Noted Publication
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Nested Hierarchical Group-wise Registration with a Graph-based Subgrouping Strategy for Efficient Template Construction
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Year: 2025
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Citation Count: N/A
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AMNet: Adaptive Multi-level Network for Deformable Registration of 3D Brain MR Images
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Year: 2023
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SDOF-GAN: Symmetric Dense Optical Flow Estimation with Generative Adversarial Networks
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Year: 2021
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Citation Count: N/A
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DGR-Net: Deep Group-wise Registration for Multi-spectral Images
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Year: 2019
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Deep Group-wise Registration for Multi-spectral Images from Fundus Images
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Year: 2019
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Citation Count: N/A
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A Framework for Brain Template Generation by Hierarchical Group-wise Image Registration
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Year: 2023
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Citation Count: N/A
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SymReg-GAN: Symmetric Image Registration with Generative Adversarial Networks
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Year: 2021
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Citation Count: N/A
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Image Matting with Deep Gaussian Process
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Year: 2022
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Citation Count: N/A
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Regional Radiomics Similarity Networks (R2SNs) in the Human Brain: Reproducibility, Small-world Properties and a Biological Basis
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Year: 2021
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Citation Count: N/A
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Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment
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Year: 2022
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Citation Count: 14
Conclusion
Dr. Tongtong Che is a rising researcher in the field of medical image analysis, with a strong foundation in deep learning and biomedical engineering. Her academic path from undergraduate to postdoctoral research has been marked by consistent excellence, culminating in numerous high-impact publications and prestigious research grants. She bridges the gap between algorithm development and clinical application, contributing significantly to the evolving landscape of neuroimaging and precision diagnostics. Her work not only advances the theoretical framework of image registration but also has tangible applications in understanding brain development and supporting clinical decision-making. With a growing international profile and a solid research portfolio, Dr. Che is well-positioned to lead interdisciplinary collaborations and tackle complex challenges in healthcare technologies. Her future endeavors aim to enhance diagnostic imaging tools and foster AI-driven innovations for personalized medicine. Dr. Che exemplifies the next generation of researchers driving innovation at the intersection of technology, medicine, and data science.