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Abstract #2956

CloudBrain-LabelAI: An Online Intelligent Medical Imaging Annotation and Training Platform

Bangjun Chen1, Jian Wang1, Yirong Zhou1, Yu Hu1, Shuxian Niu1, Biao Qu2, Di Guo3, Jingjing Xu1, Jiyang Dong1, and Xiaobo Qu1
1Biomedical Intelligent Cloud R&D Center, Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, 2Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China, 3School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, SegmentationDeep learning and cloud computing technologies have shown remarkable performance in medical imaging research. Using deep learning for clinical research requires a programming foundation, while the annotation of image data is a highly specialized and time-consuming process. In this work, we develop a high-performance online medical image annotation and training platform (CloudBrain-LabelAI). It provides medical image researchers with an efficient image annotation platform for the rapid construction of datasets for deep learning. Meanwhile, it provides codeless image segmentation training and prediction based on cloud computing, which greatly reduces the threshold of medical image segmentation efforts and simplifies the overall workflow.

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