Huili Zhang | Computer Science and Artificial Intelligence | Innovative Research Award

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Innovative Research Award

Huili Zhang
Shanghai University, China

Huili Zhang
Affiliation Shanghai University
Country China
Scopus ID 58607120700
Documents 17
Citations 223
h-index 8
Subject Area Computer Science and Artificial Intelligence
Event Top Teachers Awards
ORCID 0000-0002-3336-1756

The Innovative Research Award recognizes researchers whose scholarly activities demonstrate originality, methodological rigor, and measurable impact within their respective disciplines. Huili Zhang of Shanghai University has established a research profile centered on artificial intelligence, medical image analysis, radiomics, and intelligent diagnostic systems. Through interdisciplinary collaboration and the application of advanced machine learning methods to healthcare challenges, Zhang has contributed to the development of computational frameworks that support disease detection, classification, and clinical decision-making.[1]

Abstract

Huili Zhang’s research integrates artificial intelligence and medical imaging technologies to improve diagnostic accuracy and predictive modeling in healthcare. Her publications address multimodal ultrasound analysis, radiomics, deep learning, and knowledge distillation techniques, emphasizing clinically relevant solutions for cancer diagnosis and treatment evaluation. The body of work demonstrates a consistent focus on translating computational innovation into practical medical applications.[2]

Keywords

Artificial Intelligence, Deep Learning, Medical Imaging, Radiomics, Ultrasound Diagnostics, Knowledge Distillation, Computer-Aided Diagnosis, Healthcare Analytics.

Introduction

The convergence of artificial intelligence and healthcare has created opportunities for improved diagnostic efficiency and personalized treatment strategies. Within this evolving landscape, Huili Zhang has contributed to research that applies machine learning and image-based analytics to complex clinical problems. Her studies demonstrate the growing importance of data-driven methodologies in modern medical practice.[3]

Research Profile

As a researcher affiliated with Shanghai University, Zhang has developed expertise in computer science and artificial intelligence with a strong emphasis on biomedical applications. Her scholarly record includes peer-reviewed publications focused on multimodal imaging, radiomics-based prediction models, and intelligent healthcare systems. The available bibliometric indicators demonstrate growing academic influence across interdisciplinary domains.[1]

Research Contributions

  • Development of multi-view and multimodal deep learning frameworks for liver cancer diagnosis using ultrasound imaging.
  • Advancement of generalized knowledge distillation approaches for medical image interpretation.
  • Creation of MRI-based radiomics models for differentiating spinal multiple myeloma from metastatic lesions.
  • Application of dual-modal ultrasound and molecular data integration for predicting chemotherapy response in breast cancer patients.
  • Research into deep learning radiomics for distinguishing benign and malignant breast conditions.

Publications

  1. Multi-view doubly supervised knowledge distillation for diagnosis of liver cancers with imbalanced ultrasound imaging modalities (2026).
  2. Multi-View Disentanglement-based Bidirectional Generalized Distillation for Diagnosis of Liver Cancers with Ultrasound Images (2024).
  3. Radiomics Model Based on MRI to Differentiate Spinal Multiple Myeloma from Metastases: A Two-center Study (2024).
  4. Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer (2023).
  5. Deep Learning Radiomics of Ultrasonography for Differentiating Sclerosing Adenosis from Breast Cancer (2023).

Research Impact

The research output attributed to Zhang reflects a commitment to improving diagnostic workflows through advanced computational techniques. By combining machine learning, radiomics, and multimodal imaging data, her work contributes to enhanced disease characterization and supports evidence-based clinical decision-making. Citation activity and publication placement indicate recognition within the scientific community.[4]

Award Suitability

Huili Zhang’s research portfolio aligns with the objectives of the Innovative Research Award due to its interdisciplinary nature, methodological innovation, and relevance to healthcare technology. The integration of artificial intelligence with clinical imaging illustrates a forward-looking approach that addresses contemporary challenges in medical diagnostics while contributing to scientific advancement.[5]

Conclusion

Huili Zhang represents a growing cohort of researchers leveraging artificial intelligence to transform healthcare diagnostics. Her contributions to medical imaging, radiomics, and deep learning demonstrate both scholarly rigor and practical relevance. These achievements support recognition through the Innovative Research Award and reflect continued potential for future scientific impact.

References

  1. Elsevier. (n.d.). Scopus author details: Huili Zhang, Author ID 58607120700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58607120700
  2. Zhang, H. (2026). Multi-view doubly supervised knowledge distillation for diagnosis of liver cancers with imbalanced ultrasound imaging modalities.
    DOI: https://doi.org/10.1016/j.engappai.2026.115252
  3. Zhang, H. (2024). Multi-View Disentanglement-based Bidirectional Generalized Distillation for Diagnosis of Liver Cancers with Ultrasound Images.
    DOI: https://doi.org/10.1016/j.ipm.2024.103855
  4. Zhang, H. (2024). Radiomics Model based on MRI to Differentiate Spinal Multiple Myeloma from Metastases: A Two-center Study.
    DOI: https://doi.org/10.1016/j.jbo.2024.100599
  5. Zhang, H. (2023). Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.
    DOI: https://doi.org/10.1016/j.acra.2023.03.036
  6. Zhang, H. (2023). Deep Learning Radiomics of Ultrasonography for Differentiating Sclerosing Adenosis from Breast Cancer.
    DOI: https://doi.org/10.3233/CH-221608

Mingyue Zhang | Artificial Intelligence | Best Researcher Award

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Dr. Mingyue Zhang | Artificial Intelligence | Best Researcher Award

University of South China | China

Dr. Mingyue Zhang, Ph.D., is a dynamic researcher in computer vision, intelligent systems, and human–computer interaction, currently serving in the field of computer science with a focus on gesture recognition and lightweight deep learning models for edge devices. His research integrates advanced computer vision algorithms with human–machine collaboration, emphasizing intelligent gesture recognition for rehabilitation training, embedded AI, and Internet of Things (IoT)-enabled systems. Dr. Zhang has significantly contributed to developing efficient algorithms such as lightweight convolutional neural networks, adaptive Kalman filtering, and multi-sensor fusion frameworks, which enhance real-time performance in gesture estimation, object tracking, and assistive technologies. His innovative work bridges the gap between deep learning theory and practical deployment on embedded systems and mobile platforms. With over 25 publications in high-impact journals indexed in SCI and EI, including IEEE Internet of Things Journal, Expert Systems with Applications, IEEE Access, and Journal of Supercomputing, his research has achieved growing academic recognition. Dr. Zhang’s work has been widely cited across the global impact of his contributions in computer vision and artificial intelligence. His research is currently supported by the Hunan Provincial Department of Science and Technology, focusing on intelligent gesture recognition in rehabilitation. In recognition of his outstanding contributions to scientific innovation and scholarly excellence, Dr. Zhang is honored with the Best Researcher Award for his pioneering advancements in AI-driven human–computer interaction and lightweight network modeling.

Publication Profile

Orcid

Featured Publications

  • Jiang, C., Zhang, M., Wang, Y., & Zhang, A. (2025). AHMOT: Adaptive Kalman Filtering and Hierarchical Data Association for 3D Multi-Object Tracking in IoT-Enabled Autonomous Vehicles. IEEE Internet of Things Journal.

  • Zhang, M., Zhou, Z., Tao, X., & Deng, M. (2023). Hand pose estimation based on fish skeleton CNN: Application in gesture recognition. Journal of Intelligent & Fuzzy Systems, 44, 8029–8042.

  • Zhang, M., Zhou, Z., Wang, T., & Zhou, W. (2023). A lightweight network deployed on ARM devices for hand gesture recognition. IEEE Access, 11, 45493–45503.

  • Zhang, M., Zhou, Z., & Deng, M. (2022). Cascaded hierarchical CNN for 2D hand pose estimation from a single color image. Multimedia Tools and Applications, 81, 25745–25763.

  • Zhang, M., & Zhou, Z. (2025). Speed-accuracy trade-off in lightweight-based hand pose estimation. The Journal of Supercomputing, 81, 1212

Jinping Xue | Artificial Intelligence | Best Researcher Award

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Ms. Jinping Xue | Artificial Intelligence | Best Researcher Award

Renergy Overseas Limited | China

Ms. Jinping Xue is an emerging researcher and engineer specializing in the integration of Artificial Intelligence with sustainable urban environments. Her research focuses on Smart Cities, Industrial AI, Edge Intelligence, and Environmental AI—fields that explore how data-driven intelligence can transform urban infrastructure into more adaptive, efficient, and sustainable systems. She has made significant contributions to the development of a privacy-preserving AI framework for smart city environmental monitoring, integrating federated learning with LSTM and genetic algorithms to achieve high pollution source traceability accuracy while maintaining data privacy. Her interdisciplinary expertise bridges environmental modeling, urban systems optimization, and AI-driven perception technologies, contributing to innovative solutions in sustainable city management and smart mobility. Xue’s scholarly work demonstrates a strong interest in the application of distributed sensing and federated learning for pollution source analysis, contributing to the broader goal of achieving clean, data-secure, and intelligent urban ecosystems. She has collaborated with experts in environmental sensing and intelligent perception to enhance the real-time adaptability of urban monitoring systems. Her publications, indexed in Scopus and Google Scholar, reflect her growing impact in the domains of computational intelligence and environmental systems. With research outputs recognized in peer-reviewed international journals such as Sensors, her work has begun to gain citations across the environmental AI research community. Her citation records are progressively expanding, with verified documentation and indexing available through Scopus and Google Scholar, and her h-index count demonstrates her early but impactful research trajectory. Through her interdisciplinary approach, Xue continues to push the boundaries of AI applications for sustainable development and smart city transformation.

Publication Profile

Google Scholar

Featured Publications

Xue, J., Hu, X., Liu, Q., Yin, C., Ni, P., & Bo, X. (2025). Air pollutant traceability based on federated learning of edge intelligent perception agents. Sensors, 25(19), 6119.