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Guest Editorial Special Section on Learning With Multimodal Data for Biomedical Informatics
IEEE Transactions on Circuits and Systems for Video Technology ( IF 8.3 ) Pub Date : 5-4-2022 , DOI: 10.1109/tcsvt.2022.3160751
Zhangyang Wang 1 , Vishal Patel 2 , Bing Yao 3 , Steve Jiang 4 , Huimin Lu 5 , Yang Shen 6
Affiliation  

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.

中文翻译:


客座编辑关于生物医学信息学多模态数据学习的特别章节



在 IEEE Transactions on Circuits and Systems for Video Technology 的这个特别章节中,我们很荣幸地介绍新兴的先进机器学习和数据分析算法,旨在促进生物医学中图像/视频处理、文本/语音理解和多模态学习之间的协同作用信息学。我们的目标是 1) 引入新颖的数据驱动模型,通过无缝集成从成像系统、实验室和可穿戴设备以及其他相关医疗设备收集的医疗数据,加速生物医学领域的知识发现; 2)促进新的多模式学习系统的开发,以提高医疗质量和患者安全; 3)促进生物医学信息学的新应用,这些应用可以利用多模态数据和机器学习的集成或从中受益。
更新日期:2024-08-26
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