Guest Editorial Special Section on Learning With Multimodal Data for Biomedical Informatics

被引:0
|
作者
Wang, Zhangyang [1 ]
Patel, Vishal [2 ]
Yao, Bing [3 ]
Jiang, Steve [4 ]
Lu, Huimin [5 ]
Shen, Yang [6 ]
机构
[1] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA
[2] Johns Hopkins Univ, Dept Elect & Comp Engn, Baltimore, MD 21218 USA
[3] Oklahoma State Univ, Dept Ind Engn & Management, Stillwater, OK 70047 USA
[4] Univ Texas Southwestern Med Ctr Dallas, Div Med Phys & Engn, Dallas, TX 75390 USA
[5] Kyushu Inst Technol, Dept Mech & Control Engn, Kitakyushu, Fukuoka 8048550, Japan
[6] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA
关键词
D O I
10.1109/TCSVT.2022.3160751
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this Special Section of the IEEE Transactions on Circuits and Systems for Video Technology, it is our honor to present emerging advanced machine learning and data analytics algorithms aiming at catalyzing synergies among image/video processing, text/speech understanding, and multimodal learning in biomedical informatics. Our goals are to 1) introduce novel data-driven models to accelerate knowledge discovery in biomedicine through the seamless integration of medical data collected from imaging systems, laboratory and wearable devices, as well as other related medical devices; 2) promote the development of new multi-modal learning systems to enhance the healthcare quality and patient safety; and 3) promote new applications in biomedical informatics that can leverage or benefits from the integration of multi-modal data and machine learning.
引用
收藏
页码:2508 / 2511
页数:4
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