Yuanyuan QIN | Medical Image Analysis | Best Researcher Award

Dr . Yuanyuan QIN | Medical Image Analysis | Best Researcher Award

Associate Chief Physician, Associate Professor at  Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , China

Dr. Yuanyuan Qin is a distinguished Associate Chief Physician and Associate Professor in the Department of Radiology at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. With a strong foundation in clinical radiology and advanced neuroimaging, she has dedicated her career to investigating brain disorders such as Alzheimer’s disease, Parkinson’s disease, and post-COVID neurological changes. Her research integrates multimodal imaging techniques with cognitive neuroscience and machine learning, emphasizing non-invasive diagnostic innovations. A recipient of national research funding and provincial awards, Dr. Qin has published extensively in leading international journals, with several high-impact and highly cited articles. She has also demonstrated leadership in academic teaching through the adoption of blended learning methodologies. Her interdisciplinary expertise, clinical insights, and research productivity make her a recognized contributor to the fields of radiology and neuroscience, with ongoing work focused on understanding neural mechanisms underlying cognitive decline and aging-related diseases.

Professional Profile 

Education🎓

Dr. Yuanyuan Qin pursued her advanced medical education at Huazhong University of Science and Technology, completing a prestigious Combined Master-PhD program between 2008 and 2013. Her doctoral training emphasized advanced neuroimaging techniques, with research exploring structural and functional brain alterations in neurodegenerative conditions. During her PhD, she was selected for a one-year joint PhD training program at the esteemed Johns Hopkins University in the United States (2011–2012), where she gained international exposure to state-of-the-art imaging methodologies and collaborative research environments. Her interdisciplinary education integrated clinical radiology, neuroscience, and data-driven analysis, laying a strong foundation for her later research on cognitive disorders and aging. This cross-institutional and cross-national academic background not only enriched her scientific expertise but also cultivated her capacity to approach radiological challenges from both a clinical and research perspective. Her academic training continues to inform her innovative work in diagnostic imaging and cognitive neurodegeneration.

Professional Experience📝

Dr. Yuanyuan Qin has built a progressive and impactful professional career in radiology at Tongji Hospital. Starting in 2013 as a Resident Physician, she rapidly advanced through roles as an Attending Physician (2014–2019), Associate Chief Physician (2019–2020), and ultimately to Associate Professor (2020–present). Throughout her career, she has been deeply engaged in clinical diagnostics, medical imaging interpretation, and mentoring medical students and interns. Her dual roles in academic and clinical settings have allowed her to integrate patient-centered care with research-led innovation. Dr. Qin’s experience spans routine radiological evaluations to complex imaging studies in patients with neurological and neurodegenerative conditions. She has actively contributed to improving radiology internship training programs through digital platforms and 3D simulation tools. Her leadership within the department is recognized not only in her clinical acumen but also in fostering collaborative research projects and guiding junior physicians and researchers in translational imaging studies.

Research Interest🔎

Dr. Yuanyuan Qin’s research interests lie at the intersection of neuroimaging, cognitive neuroscience, and clinical radiology. Her primary focus is on understanding the neural mechanisms of neurodegenerative diseases, particularly Alzheimer’s and Parkinson’s, through advanced magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and functional connectivity analysis. She is also deeply interested in the application of repetitive transcranial magnetic stimulation (rTMS) combined with cognitive training as a therapeutic and diagnostic tool. Recently, her research has extended into the neurological impacts of COVID-19, exploring long-term cerebral microstructure changes in asymptomatic patients. Her work frequently integrates artificial intelligence, deep learning, and image quantification methods to improve diagnostic precision. Dr. Qin is committed to developing non-invasive imaging biomarkers that can track disease progression and predict cognitive decline. Her interdisciplinary approach bridges clinical needs with technological advancement, contributing valuable insights into early detection and intervention strategies for aging-related cognitive disorders.

Award and Honor🏆

Dr. Yuanyuan Qin has been widely recognized for her academic excellence and scientific contributions. Notably, she received the First Prize in the 2019 Hubei Provincial Science and Technology Progress Award for her innovative work on the integration of structural and functional MRI in brain development and aging-related diseases. In teaching, she earned the Second Prize in the 2020 Young Teacher Teaching Competition at Huazhong University of Science and Technology, highlighting her commitment to educational innovation. As a principal investigator, she has secured multiple competitive grants, including a General Program and a Youth Project from the National Natural Science Foundation of China (NSFC). Her work has garnered national attention and peer acknowledgment, with multiple publications cited widely in top-tier journals. Her research article in the Journal of Clinical Investigation is listed as a Highly Cited Article, further validating her impact in the fields of radiology and neuroscience.

Research Skill🔬

Dr. Yuanyuan Qin possesses a robust portfolio of research skills, especially in neuroimaging analysis, multimodal MRI, and diffusion tensor imaging (DTI). She has extensive expertise in image processing platforms such as 3D-Slicer, FSL, and SPM, along with experience in deep learning algorithms for radiological quantification. Her technical strengths extend to designing and conducting longitudinal studies, particularly in evaluating cognitive interventions like rTMS paired with cognitive training in Alzheimer’s patients. She demonstrates proficiency in integrating clinical data with imaging outcomes to derive meaningful correlations for disease diagnosis and prognosis. Additionally, she has contributed to the development of automated MRI quantification pipelines, including those for Parkinsonism index assessment. Her interdisciplinary methods often incorporate statistical modeling, functional connectivity analysis, and AI-based imaging biomarker discovery. These research capabilities position her as a key contributor in translating complex neuroimaging insights into real-world clinical applications.

Conclusion💡

Dr. Yuanyuan Qin is highly suitable for the Best Researcher Award based on her exceptional track record in neuroimaging research, consistent national-level funding, scientific leadership in Alzheimer’s and Parkinson’s research, and significant contributions to radiological education and practice. Her trajectory exemplifies a balance between academic rigor, innovation, and clinical relevance.

Publications Top Noted✍

  1. Title: Surface-Based Vertexwise Analysis of Morphometry and Microstructural Integrity for White Matter Tracts in Diffusion Tensor Imaging: With Application to the Corpus Callosum in Alzheimer’s Disease
    Authors: Tang, Xiaoying; Qin, Yuanyuan; Zhu, Wenzhen; Miller, Michael I.
    Year: 2017
    Citation: Human Brain Mapping, DOI: 10.1002/hbm.23491

  2. Title: Atlas-based deep gray matter and white matter analysis in Alzheimer’s disease: diffusion abnormality and correlation with cognitive function
    Authors: Qin Yuanyuan; Zhang Shun; Guo Linying; Zhang Min; Zhu Wenzhen
    Year: 2016
    Citation: Chinese Journal of Radiology, WOSUID: CSCD:5699935

  3. Title: Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer’s disease
    Authors: Tang, Xiaoying; Qin, Yuanyuan; Wu, Jiong; Zhang, Min; Zhu, Wenzhen; Miller, Michael I.
    Year: 2016
    Citation: Magnetic Resonance Imaging, DOI: 10.1016/j.mri.2016.05.001

  4. Title: Simulating the Evolution of Functional Brain Networks in Alzheimer’s Disease: Exploring Disease Dynamics from the Perspective of Global Activity
    Authors: Li, Wei; Wang, Miao; Zhu, Wenzhen; Qin, Yuanyuan; Huang, Yue; Chen, Xi
    Year: 2016
    Citation: Scientific Reports, DOI: 10.1038/srep34156

  5. Title: Frequency-specific Alterations of Large-scale Functional Brain Networks in Patients with Alzheimer’s Disease
    Authors: Qin, Yuan-Yuan; Li, Ya-Peng; Zhang, Shun; Xiong, Ying; Guo, Lin-Ying; Yang, Shi-Qi; Yao, Yi-Hao; Li, Wei; Zhu, Wen-Zhen
    Year: 2015
    Citation: Chinese Medical Journal, DOI: 10.4103/0366-6999.151654

  6. Title: Frequency-specific Alterations of Large-scale Functional Brain Networks in Patients with Alzheimer’s Disease (Correction)
    Authors: Qin, Y. Y.; Li, Y. P.; Zhang, S.; Xiong, Y.; Guo, L. Y.; Yang, S. Q.
    Year: 2015
    Citation: Chinese Medical Journal, DOI: 10.4103/0366-6999.156150

  7. Title: An Efficient Approach for Differentiating Alzheimer’s Disease from Normal Elderly Based on Multicenter MRI Using Gray-Level Invariant Features
    Authors: Li, Muwei; Oishi, Kenichi; He, Xiaohai; Qin, Yuanyuan; Gao, Fei; Mori, Susumu
    Year: 2014
    Citation: PLOS ONE, DOI: 10.1371/journal.pone.0105563

  8. Title: Discriminative Analysis of Multivariate Features from Structural MRI and Diffusion Tensor Images
    Authors: Li, Muwei; Qin, Yuanyuan; Gao, Fei; Zhu, Wenzhen; He, Xiaohai
    Year: 2014
    Citation: Magnetic Resonance Imaging, DOI: 10.1016/j.mri.2014.05.008

  9. Title: Exploring the Functional Brain Network of Alzheimer’s Disease: Based on the Computational Experiment
    Authors: Li, YaPeng; Qin, Yuanyuan; Chen, Xi; Li, Wei
    Year: 2013
    Citation: PLOS ONE, DOI: 10.1371/journal.pone.0073186

  10. Title: Gross Feature Recognition of Anatomical Images Based on Atlas Grid (GAIA)
    Authors: Qin, Yuan-Yuan; Hsu, Johnny T.; Yoshida, Shoko; Faria, Andreia V.; Oishi, Kumiko; et al.
    Year: 2013
    Citation: NeuroImage: Clinical, DOI: 10.1016/j.nicl.2013.08.006

  11. Title: In vivo Quantitative Whole-brain Diffusion Tensor Imaging Analysis of APP/PS1 Transgenic Mice
    Authors: Qin, Yuan-Yuan; Li, Mu-Wei; Zhang, Shun; Zhang, Yan; Zhao, Ling-Yun; et al.
    Year: 2013
    Citation: Neuroradiology, DOI: 10.1007/s00234-013-1195-0

  12. Title: The Functional Brain Network Changes of Alzheimer’s Disease
    Authors: Li YaPeng; Qin YuanYuan; Li Wei
    Year: 2013
    Citation: Chinese Journal of Medical Physics, WOSUID: CSCD:5004621

  13. Title: Voxel-Based Diffusion Tensor Imaging of an APP/PS1 Mouse Model of Alzheimer’s Disease
    Authors: Shu, Xiaogang; Qin, Yuan-Yuan; Zhang, Shun; Jiang, Jing-Jing; Zhang, Yan; et al.
    Year: 2013
    Citation: Molecular Neurobiology, DOI: 10.1007/s12035-013-8418-6

  14. Title: Stromal Cell-Derived Factor 1 Alpha Decreases Beta-Amyloid Deposition in Alzheimer’s Disease Mouse Model
    Authors: Wang, Qi; Xu, Yi; Chen, Jin-Cao; Qin, Yuan-Yuan; Liu, Mao; et al.
    Year: 2012
    Citation: Brain Research, DOI: 10.1016/j.brainres.2012.04.011

 

Dibyalekha Nayak | Computer vision | Women Researcher Award

Dr . Dibyalekha Nayak | Computer vision | Women Researcher Award

Assistant professor at Shah and Anchor Kutchhi Engineering College, India

Dr. Dibyalekha Nayak is a dedicated academician and emerging researcher with deep expertise in image processing, adaptive compression, and VLSI design. Her professional journey is marked by a strong commitment to teaching, scholarly research, and technological advancement. With over a decade of teaching experience and a recently completed Ph.D. from KIIT University, Bhubaneswar, her research has produced several publications in SCI-indexed journals and international conferences. Dr. Nayak’s contributions reflect an interdisciplinary approach, combining deep learning techniques with low-power hardware design to address complex challenges in wireless sensor networks and multimedia systems. She has actively participated in faculty development programs and technical workshops, continuously upgrading her knowledge. Her professional philosophy emphasizes ethics, hard work, and continuous learning. Currently serving as an Assistant Professor at Shah and Anchor Kutchi Engineering College in Mumbai, she aspires to make impactful contributions to the field of electronics and communication through research, innovation, and collaboration.

Professional Profile 

Education🎓

Dr. Dibyalekha Nayak holds a Ph.D. in Image Processing from the School of Electronics at KIIT University, Bhubaneswar, where she completed her research between September 2018 and May 2024. Her doctoral work focused on advanced techniques in image compression and saliency detection using deep learning and compressive sensing. She completed her Master of Technology (M.Tech) in VLSI Design from Satyabhama University, Chennai, in 2011, graduating with a commendable CGPA of 8.33. Prior to that, she earned her Bachelor of Engineering (B.E.) in Electronics and Telecommunication from Biju Patnaik University of Technology (BPUT), Odisha, in 2008, with a CGPA of 6.5. Her academic background provides a strong foundation in both theoretical electronics and practical applications in image processing and circuit design. The combination of image processing and VLSI design throughout her academic journey has enabled her to engage in cross-disciplinary research and foster innovation in both hardware and software domains.

Professional Experience📝

Dr. Dibyalekha Nayak has accumulated over 12 years of rich academic experience in various reputed engineering institutions across India. Currently, she serves as an Assistant Professor at Shah and Anchor Kutchi Engineering College, Mumbai, affiliated with Mumbai University, where she joined in July 2024. Prior to this, she worked as a Research Scholar at KIIT University (2018–2024), contributing significantly to image processing research. Her earlier roles include Assistant Professor positions at institutions such as College of Engineering Bhubaneswar (2016–2018), SIES Graduate School of Technology, Mumbai (2014), St. Francis Institute of Technology, Mumbai (2013), and Madha Engineering College, Chennai (2011–2012). Across these roles, she has taught a variety of undergraduate and postgraduate courses, supervised student projects, and contributed to departmental development. Her teaching areas span digital electronics, VLSI design, image processing, and communication systems, demonstrating a strong alignment between her teaching and research activities.

Research Interest🔎

Dr. Dibyalekha Nayak’s research interests lie at the intersection of image processing, deep learning, and VLSI design, with a special focus on adaptive compression, saliency detection, and compressive sensing. Her doctoral research addressed the development of innovative, low-complexity algorithms for image compression using techniques like block truncation coding and DCT, tailored for wireless sensor network applications. She is also deeply interested in integrating deep learning frameworks into image enhancement and compression tasks to improve performance in real-world environments. Additionally, her background in VLSI design supports her interest in low-power hardware architectures for efficient implementation of image processing algorithms. Dr. Nayak is particularly motivated by research problems that bridge the gap between theoretical innovation and practical implementation, especially in the fields of embedded systems and multimedia communication. Her interdisciplinary research aims to create scalable, energy-efficient, and intelligent solutions for future communication and sensing technologies.

Award and Honor🏆

While Dr. Dibyalekha Nayak’s profile does not explicitly mention formal awards or honors, her scholarly achievements speak volumes about her academic excellence and dedication. She has published multiple research articles in prestigious SCI and Web of Science indexed journals such as Multimedia Tools and Applications, Mathematics, and Computers, reflecting the quality and impact of her research. She has been actively involved in reputed international conferences including IEEE and Springer Lecture Notes, where she has presented and published her research findings. Her work on saliency-based image compression and fuzzy rule-based adaptive block compressive sensing has received commendation for its innovation and applicability. Furthermore, her selection and sustained work as a Research Scholar at KIIT University for over five years highlights the recognition she has earned within academic circles. Her consistent participation in technical workshops, faculty development programs, and collaborations also demonstrate her growing reputation and standing in the field of electronics and image processing.

Research Skill🔬

Dr. Dibyalekha Nayak possesses a versatile and robust set of research skills aligned with modern-day challenges in image processing and electronics. She is proficient in developing image compression algorithms, saliency detection models, and adaptive techniques using block truncation coding, fuzzy logic, and DCT-based quantization. Her technical expertise extends to deep learning architectures tailored for image enhancement and compressive sensing in wireless sensor networks. Additionally, she has a strong command of VLSI design methodologies, enabling her to work on low-power circuit design and hardware implementation strategies. Dr. Nayak is also skilled in scientific programming, using tools such as MATLAB and Python, along with LaTeX for research documentation. She has a clear understanding of research methodologies, simulation frameworks, and performance analysis metrics. Her experience in preparing manuscripts for SCI-indexed journals and conference presentations showcases her technical writing abilities. Overall, her analytical mindset and hands-on skills make her a competent and impactful researcher.

Conclusion💡

Dr. Dibyalekha Nayak is a highly dedicated and emerging researcher in the fields of Image Processing, Deep Learning, and VLSI. Her academic journey reflects perseverance, scholarly depth, and a clear focus on impactful research. Her SCI-indexed publications, teaching experience, and cross-domain knowledge make her a deserving candidate for the Best Researcher Award.

Publications Top Noted✍

  • Title: Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application
    Authors: D. Nayak, K. Ray, T. Kar, S.N. Mohanty
    Journal: Mathematics, Volume 11, Issue 7, Article 1660
    Year: 2023
    Citations: 6

  • Title: A novel saliency based image compression algorithm using low complexity block truncation coding
    Authors: D. Nayak, K.B. Ray, T. Kar, C. Kwan
    Journal: Multimedia Tools and Applications, Volume 82, Issue 30, Pages 47367–47385
    Year: 2023
    Citations: 4

  • Title: Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization
    Authors: D. Nayak, K. Ray, T. Kar, C. Kwan
    Journal: Computers, Volume 11, Issue 7, Article 110
    Year: 2022
    Citations: 3

  • Title: Sparsity based Adaptive BCS color image compression for IoT and WSN Application
    Authors: D. Nayak, T. Kar, K. Ray
    Journal: Signal, Image and Video Processing, Volume 19, Issue 8, Pages 1–7
    Year: 2025

  • Title: Hybrid Image Compression Using DCT and Autoencoder
    Authors: D. Nayak, T. Kar, K. Ray, J.V.R. Ravindra, S.N. Mohanty
    Conference: 2024 IEEE Pune Section International Conference (PuneCon), Pages 1–6
    Year: 2024

  • Title: Performance Comparison of Different CS based Reconstruction Methods for WSN Application
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: 2021 IEEE 2nd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)
    Year: 2021

  • Title: A Comparative Analysis of BTC Variants
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: Proceedings of International Conference on Communication, Circuits, and Systems (LNEE, Springer)
    Year: 2021

  • Title: Low Power Error Detector Design by using Low Power Flip Flops Logic
    Authors: D. Chaini, P. Malgi, S. Lopes
    Journal: International Journal of Computer Applications, ISSN 0975-8887
    Year: 2014