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Proceedings Paper

A method of dividing clinical data set for medical image AI training
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Paper Abstract

In the AI training, the data set is always divided into training set and test set at random, but the clinical image data from hospitals is different from the public data set. The division of public data set is reasonably divided and evenly distributed after many experiments. Accurate understanding of the data distribution directly affects the training model quality. So we proposed a new method of dividing clinical data set based on distance metric learning of the Gaussian mixture model to obtain more reasonable data set divisions. The distance metric learning based on deep neural network, first embeds data into a new metric space, then in the metric space uses in-depth mining based on data characteristics, calculates the distance between samples, finally compares the differences. The method can accurately know the data distribution characteristics to a certain extent. Under the condition of understanding the data distribution characteristics, more reasonable divisions can be obtained. That can greatly affect the accuracy and generalization performance of the models we trained.

Paper Details

Date Published: 2 March 2020
PDF: 10 pages
Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 113180N (2 March 2020); doi: 10.1117/12.2549552
Show Author Affiliations
Dezhong Zheng, The Shanghai Institute of Technical Physics (China)
Univ. of Chinese Academy of Sciences (China)
Yuanyuan Yang, The Shanghai Institute of Technical Physics (China)
Wentao Li, Fudan Univ. Shanghai Cancer Ctr. (China)


Published in SPIE Proceedings Vol. 11318:
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Thomas M. Deserno, Editor(s)

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