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The lack of labeled image data poses a serious challenge to the application of artificial intelligence (AI) in medical image diagnosis. Medical image notes contain valuable patient information that could be used to label images for machine learning tasks. However, most image note texts are unstructured with heterogeneity and short-paragraph characters, which fail traditional keyword-based techniques. We utilized a deep learning approach to recover missing labels for medical image notes automatically by using a combination of deep word embedding and deep neural network classifiers. Bidirectional encoder representations from transformers trained on medical image notes corpus (MinBERT) were proposed. We applied the proposed techniques to two typical classification tasks: Medical image type identification and clinical diagnosis identification. The two methods significantly outperformed baseline methods and presented high accuracies of 99.56 % and 99.72 % in image type identification and of 94.56 % and 92.45 % in clinical diagnosis identification. Visualization analysis further indicated that word embedding could efficiently capture semantic similarities and regularities across diverse expressions. Results indicated that our proposed framework could accurately recover the missing label information of medical images through the automatic extraction of electronic medical record information. Hence, it could serve as a powerful tool for exploring useful training data in various medical AI applications.


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Classification of Medical Image Notes for Image Labeling by Using MinBERT

Show Author's information Bokai Yang1,3Yujie Yang1,3Qi Li1,3Denan Lin2Ye Li1,3Jing Zheng2( )Yunpeng Cai1,3( )
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Shenzhen Health Development Research and Data Management Center, Shenzhen 518055, China
University of Chinese Academy of Sciences, Beijing 100049, China

Abstract

The lack of labeled image data poses a serious challenge to the application of artificial intelligence (AI) in medical image diagnosis. Medical image notes contain valuable patient information that could be used to label images for machine learning tasks. However, most image note texts are unstructured with heterogeneity and short-paragraph characters, which fail traditional keyword-based techniques. We utilized a deep learning approach to recover missing labels for medical image notes automatically by using a combination of deep word embedding and deep neural network classifiers. Bidirectional encoder representations from transformers trained on medical image notes corpus (MinBERT) were proposed. We applied the proposed techniques to two typical classification tasks: Medical image type identification and clinical diagnosis identification. The two methods significantly outperformed baseline methods and presented high accuracies of 99.56 % and 99.72 % in image type identification and of 94.56 % and 92.45 % in clinical diagnosis identification. Visualization analysis further indicated that word embedding could efficiently capture semantic similarities and regularities across diverse expressions. Results indicated that our proposed framework could accurately recover the missing label information of medical images through the automatic extraction of electronic medical record information. Hence, it could serve as a powerful tool for exploring useful training data in various medical AI applications.

Keywords: convolutional neural network, word embedding, electronic medical record, MinBERT, medical image labeling

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Received: 05 January 2022
Revised: 29 April 2022
Accepted: 18 May 2022
Published: 06 January 2023
Issue date: August 2023

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© The author(s) 2023.

Acknowledgements

This work was supported in part by the Shenzhen Science and Technology Program (No. JCYJ20180703145002040), the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB38050100) and the Shenzhen Science and Technology Program (No. JCYJ20180507182818013).

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