Dr. Teng Huang, Guangzhou University, China
Dr. Teng Huang is a distinguished researcher at Guangzhou University, China, specializing in Blockchain, Smart Contracts, and Medical Image Analysis. His contributions span diverse areas, including Comprehensive Transformer Integration Networks (CTIN), endoscopic disease segmentation, and intelligent 3D tumor segmentation. His expertise extends to remote sensing image change detection, privacy-preserving AI, and recommender systems. Dr. Huang has authored numerous high-impact IEEE and Springer publications, advancing cutting-edge AI applications. His research focuses on developing efficient and scalable AI solutions for medical imaging, security, and remote sensing, positioning him as a leading innovator in computational intelligence.
Professional Profile 🌍
Orcid
Suitability for Best Researcher Award 🏆
Dr. Teng Huang is an exceptional candidate for the Best Researcher Award, given his groundbreaking contributions in blockchain technology, smart contracts, and AI-driven medical imaging. His highly cited research in medical segmentation, secure AI architectures, and remote sensing innovations underscores his impact on academia and industry. Dr. Huang’s work in privacy-preserving AI and adversarial learning is transforming cybersecurity and healthcare analytics. With an extensive publication record in IEEE Transactions and Springer, he has significantly advanced computational efficiency, security, and AI-powered medical diagnostics, making him a standout nominee for this prestigious recognition.
Professional Experience 👨🏫
Dr. Teng Huang is a senior researcher and faculty member at Guangzhou University, where he leads projects on medical AI, blockchain security, and computational intelligence. He has collaborated on multinational research initiatives, developing advanced AI frameworks for ultrasound and MRI analysis, tumor segmentation, and privacy-preserving recommender systems. Dr. Huang has served as a principal investigator for high-profile studies in remote sensing, adversarial AI, and federated learning. His work has been instrumental in advancing medical diagnostics, cybersecurity protocols, and AI-driven automation, making him a sought-after expert in intelligent computing and blockchain research.
Awards & Honors 🏅
Dr. Teng Huang has received multiple accolades for his contributions to artificial intelligence, medical imaging, and cybersecurity. He has been honored with the Best Paper Award at IEEE conferences for his work on efficient breast lesion segmentation and smart contract security. He was recognized among the Top AI Researchers in China for his pioneering work on transformer-based medical diagnostics. Dr. Huang also received the Outstanding Researcher Award from Guangzhou University for his breakthroughs in blockchain and AI-driven healthcare solutions. His contributions to privacy-preserving AI and cybersecurity have earned him international recognition.
Research Focus 🔬
Dr. Teng Huang’s research is centered on Blockchain, Smart Contracts, Medical AI, and Privacy-Preserving AI. His expertise includes 3D tumor segmentation, ultrasound imaging, federated learning, adversarial AI, and remote sensing. He specializes in transformer-based architectures for medical diagnostics, lightweight AI models for resource-limited platforms, and privacy-enhanced encryption techniques for IoT security. His work on self-sovereign identity management and subgraph matching algorithms has significantly advanced blockchain security and data protection. Dr. Huang’s interdisciplinary approach integrates deep learning, AI-driven medical analysis, and secure computing, positioning him at the forefront of intelligent healthcare innovations.
Publication Top Notes 📖
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Comprehensive Transformer Integration Network (CTIN): Advancing Endoscopic Disease Segmentation with Hybrid Transformer Architecture
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Efficient Breast Lesion Segmentation From Ultrasound Videos Across Multiple Source-Limited Platforms
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IPM: An Intelligent Component for 3D Brain Tumor Segmentation Integrating Semantic Extractor and Pixel Refiner
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Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation
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Optimized Breast Lesion Segmentation in Ultrasound Videos Across Varied Resource-Scant Environments
- SFFAFormer: A Semantic Fusion and Feature Accumulation Approach for Remote Sensing Image Change Detection