Lingfeng Huang
Papers
1
Total Citations
4
H-Index
1
About
Lingfeng Huang is a rising researcher at the intersection of artificial intelligence and traditional medicine, with a primary focus on real-time biomedical image analysis and acupoint detection. Their most notable contribution is the development of Rt-Demt, a hybrid real-time acupoint detection model that innovatively combines Mamba and Transformer architectures. This work, published in 2024, has already garnered 4 citations, signaling early impact in the field. Huang’s research addresses the critical challenge of integrating deep learning with acupuncture practice, enabling faster and more accurate identification of acupoints for clinical and therapeutic applications. By bridging state-of-the-art sequence modeling with medical imaging, Huang is helping to modernize traditional Chinese medicine through computational efficiency and precision. Their work holds promise for advancing automated acupuncture systems and real-time health monitoring technologies. As an emerging scholar, Huang’s contributions reflect a growing trend of applying advanced AI architectures to niche medical domains, positioning them as a key figure in the future of intelligent healthcare.
Research Focus
Key Achievements
Top Papers
- 1