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Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning

  • Image & Signal Processing
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Abstract

The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patients with follow-up treatment plans and preventing lesion deterioration. In recent years, deep learning has achieved many satisfactory results in gastroscopic image classification tasks. However, most of them need a training set that consists of large numbers of images labeled by experienced experts. To reduce the image annotation burdens and improve the classification ability on small labeled gastroscopic image datasets, this study proposed a novel semi-supervised efficient contrastive learning (SSECL) classification method for esophageal disease. First, an efficient contrastive pair generation (ECPG) module was proposed to generate efficient contrastive pairs (ECPs), which took advantage of the high similarity features of images from the same lesion. Then, an unsupervised visual feature representation containing the general feature of esophageal gastroscopic images is learned by unsupervised efficient contrastive learning (UECL). At last, the feature representation will be transferred to the down-stream esophageal disease classification task. The experimental results have demonstrated that the classification accuracy of SSECL is 92.57%, which is better than that of the other state-of-the-art semi-supervised methods and is also higher than the classification method based on transfer learning (TL) by 2.28%. Thus, SSECL has solved the challenging problem of improving the classification result on small gastroscopic image dataset by fully utilizing the unlabeled gastroscopic images and the high similarity information among images from the same lesion. It also brings new insights into medical image classification tasks.

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Funding

This research was supported by the National Natural Science Foundation of China (61720106004 and 61872405) and the Key R&D Project of Sichuan Province (2020YFS0243).

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Authors and Affiliations

  1. Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Wenju Du, Nini Rao, Jiahao Yong, Yingchun Wang & Dingcan Hu

  2. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Wenju Du, Nini Rao, Jiahao Yong, Yingchun Wang & Dingcan Hu

  3. Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, 610017, China

    Tao Gan & Linlin Zhu

  4. School of Information and Communication Engineering, University Electronic Science and Technology of China, Chengdu, 610054, China

    Bing Zeng

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  1. Wenju Du
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Correspondence to Nini Rao or Tao Gan.

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Du, W., Rao, N., Yong, J. et al. Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning. J Med Syst 46, 4 (2022). https://doi.org/10.1007/s10916-021-01782-z

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