Like Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Like Li has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
Jia Lin
College of Information Science and Engineering, Northeastern University,Shenyang,China,110819
1 shared publication- Shen Yin
Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, Norway
1 shared publication - Ting Feng
College of Information Science and Engineering Northeastern University Shenyang China
1 shared publication - Xiang Li
College of Information Science and Engineering, Northeastern University,Shenyang,China,110819
1 shared publication Yu Sun
College of Information Science and Engineering, Northeastern University,Shenyang,China,110819
1 shared publication
Latest Publications
- 1 June 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Artificial Intelligence Science and Engineering
- Vol. 1 (2) , 79-97
The Synergy of Seeing and Saying: Revolutionary Advances in Multi-modality Medical Vision-Language Large Models
The application of visual-language large models in the field of medical health has gradually become a research focus. The models combine the capability for image understanding and natural language processing, and can simultaneously process multi-modality data such as medical images and medical reports. These models can not only recognize images, but also understand the semantic relationship between images and texts, effectively realize the integration of medical information, and provide strong support for clinical decision-making and disease diagnosis. The visual-language large model has good performance for specific medical tasks, and also shows strong potential and high intelligence in the general task models. This paper provides a comprehensive review of the visual-language large model in the field of medical health. Specifically, this paper first introduces the basic theoretical basis and technical principles. Then, this paper introduces the specific application scenarios in the field of medical health, including modality fusion, semi-supervised learning, weakly supervised learning, unsupervised learning, cross-domain model and general models. Finally, the challenges including insufficient data, interpretability, and practical deployment are discussed. According to the existing challenges, four potential future development directions are given.
- 1 June 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Artificial Intelligence Science and Engineering
- Vol. 1 (2) , 79-97
The Synergy of Seeing and Saying: Revolutionary Advances in Multi-modality Medical Vision-Language Large Models
The application of visual-language large models in the field of medical health has gradually become a research focus. The models combine the capability for image understanding and natural language processing, and can simultaneously process multi-modality data such as medical images and medical reports. These models can not only recognize images, but also understand the semantic relationship between images and texts, effectively realize the integration of medical information, and provide strong support for clinical decision-making and disease diagnosis. The visual-language large model has good performance for specific medical tasks, and also shows strong potential and high intelligence in the general task models. This paper provides a comprehensive review of the visual-language large model in the field of medical health. Specifically, this paper first introduces the basic theoretical basis and technical principles. Then, this paper introduces the specific application scenarios in the field of medical health, including modality fusion, semi-supervised learning, weakly supervised learning, unsupervised learning, cross-domain model and general models. Finally, the challenges including insufficient data, interpretability, and practical deployment are discussed. According to the existing challenges, four potential future development directions are given.