Mr. Yunxiang Li, UT Southwestern Medical Center, United States
Yunxiang Li is a PhD student of Medical Physics at the University of Texas Southwestern Medical Center, with a Bachelor’s degree in Computer Science and Technology from Hangzhou Dianzi University. During his tenure at Hangzhou Dianzi University, he collaborated with leading medical institutions, contributing to automatic diagnosis research in root canal therapy. His subsequent work at the IDEA Lab, University of North Carolina at Chapel Hill, focused on infant brain segmentation, resulting in a significant publication in MICCAI. Currently, at the MAIA Lab, UT Southwestern Medical Center, he leads various projects in medical image analysis, including pioneering research on diffusion models and multimodal segmentation techniques. Yunxiang has demonstrated exceptional productivity with numerous first-author papers published and under review in top-tier journals and conferences. His contributions to the field have been widely recognized, evident in his impactful projects such as Chatdoctor and LViT.
Professional Profile:
Scopus
Google Scholar
🎓 Education:
Yunxiang Li is currently pursuing his Ph.D. in Medical Physics at the University of Texas Southwestern Medical Center in Dallas, USA, a program he began in 2022. Prior to this, he completed his Bachelor’s degree in Computer Science and Technology at Hangzhou Dianzi University in Hangzhou, China, from 2018 to 2022. In 2019, Yunxiang expanded his academic horizons as a Visiting Student at The University of Adelaide in Adelaide, Australia. With a diverse educational background spanning computer science and medical physics, Yunxiang is well-equipped to undertake innovative research at the intersection of technology and healthcare.
🔬 Research Interests:
Yunxiang Li’s research interests revolve around the field of Medical Image Analysis, where he delves into various aspects such as Classification, Segmentation, Transformer in vision, Diffusion Model, and LLM. With a keen focus on advancing healthcare technology, Yunxiang’s work aims to enhance the accuracy and efficiency of medical image interpretation and processing. His expertise spans from developing classification algorithms to intricate segmentation techniques, as well as exploring innovative models like the Transformer in vision and the Diffusion Model. Through his research endeavors, Yunxiang seeks to contribute to the improvement of medical diagnostics and treatment planning, ultimately benefitting patients and healthcare professionals
💼 Experience:
Yunxiang Li’s professional journey encompasses diverse research experiences across prestigious institutions. He began his career at the Microelectronics CAD Center of Hangzhou Dianzi University, where he contributed to projects focused on automatic diagnosis of root canal therapy in collaboration with the National Clinical Research Center for Oral Diseases. Following this, Yunxiang joined the IDEA Lab at the University of North Carolina at Chapel Hill, where he worked on infant brain segmentation projects. Currently, he is engaged in groundbreaking research at the MAIA Lab of UT Southwestern Medical Center, focusing on diffusion models and LLM for medical image analysis. With each opportunity, Yunxiang has demonstrated his commitment to advancing knowledge and technology in the field of medical physics.
Publication Top Notes:
- Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge
- Authors: Y Li, Z Li, K Zhang, R Dan, S Jiang, Y Zhang
- Year: 2023
- Journal: Cureus
- Cited By: 204
- Fives: A fundus image dataset for Artificial Intelligence based vessel segmentation
- Authors: K Jin, X Huang, J Zhou, Y Li, Y Yan, Y Sun, Q Zhang, Y Wang, J Ye
- Year: 2022
- Journal: Scientific Data
- Cited By: 63
- A Cascade‐SEME network for COVID‐19 detection in chest x‐ray images
- Authors: D Lv, Y Wang, S Wang, Q Zhang, W Qi, Y Li, L Sun
- Year: 2021
- Journal: Medical Physics
- Cited By: 49
- Lvit: Language meets vision transformer in medical image segmentation
- Authors: Z Li, Y Li, Q Li, P Wang, D Guo, L Lu, D Jin, Y Zhang, Q Hong
- Year: 2023
- Journal: IEEE Transactions on Medical Imaging
- Cited By: 44
- GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
- Authors: Y Li, S Wang, J Wang, G Zeng, W Liu, Q Zhang, Q Jin, Y Wang
- Year: 2021
- Conference: MICCAI2021, Machine Learning in Medical Imaging
- Cited By: 41