Machine Learning and Big Data in Medical Image Analysis

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Guest Editors:

Prof. Heye Zhang
School of Biomedical Engineering, Sun Yat-Sen University, China

Prof. Shuo Li
Department of Medical Imaging, Western University, Canada

Call for Papers Announcement

Because of the potential of machine learning and big data in efficiently providing powerful tool for image analysis, a large number of medical image applications of machine learning using big data have been developed in the past years. However, the increasing volume of medical data has brought a great difficulty in real-time processing. Furthermore, the medical data collected via different imaging technology are so huge, which require a powerful framework to understand these data efficiently. All these barriers prohibit the translation of the machine learning technology into daily clinical practice. The rapid development of machine learning theory has stimulated different algorithms in processing big data efficiently, for example deep learning in real-time face recognition. Thus, the combination of medical image analysis and machine learning theory might have the potential to alleviate the pressure on healthcare systems. This special issue will focus on the technical challenges and future trend of Machine learning and big data in medical image analysis.

Potential topics include, but are not limited to:

• Theory and foundation research of Machine learning for medical image analysis
• Innovative and intelligent medical image processing applications
• Imbalance and unstructured medical data representation
• Machine learning algorithm in compatible embedded system for image analysis
• Intelligent understanding of medical diagnosis over medical image data.
• Security, privacy, integrity, and trust in medical image analysis
• Real-time processing of big data (e.g. CT or MRI image)
• Clouding computing for medical image analysis

Paper Submission

Full text submission due date: December 30, 2019.
Full paper should be submitted online to the journal at
Before submission, authors should carefully read over the instruction for Authors which are located at

Xu Lu, Chuan Yang, Yujing Zhang, Shanqiu Huang, Li Li, Haoqun Chen, Long Gao, Yan Ma, Wei Song
+ Abstract     + HTML     + PDF(541 KB)
Cheng Li, Xiuzhen Sun, Ming Zhao, Shen Yu, Qian Huang, Xiaoqing Zhang, Zhenxiao Huang, Shunjiu Cui, Bing Zhou
+ Abstract     + HTML     + PDF(684 KB)
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