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A semi-supervised medical image classification method based on combined pseudo-labeling and distance metric consistency

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Abstract

In medical image analysis, obtaining high-quality labeled data is expensive, and there is a large amount of unlabeled image data that is not utilized. Semi-supervised learning can use unlabeled data to improve the model performance in the presence of data scarcity medical image analysis. In this paper, we propose a semi-supervised framework for medical image classification considering both feature extraction layer information and semantic classification layer information. It is a method that includes a combined pseudo-labeling strategy and a feature distance metric consistency method. Compared with other semi-supervised classification methods, our method significantly improves the accuracy of pseudo-labels by combining the feature metric pseudo-label with the semantic classification pseudo-label and enables the model to explore deeper information by constraining the distance relations of sample features in the feature space. We conducted extensive experiments on two public medical image datasets to demonstrate that our method outperforms various state-of-the-art semi-supervised learning methods.

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Data Availibility

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

In this paper, the research is sponsored by the National Natural Science Foundation of China (61272315), the Natural Science Foundation of Zhejiang Province (LQ20F030015, LY21F020028, 2023C01040), and the Foundation of Zhejiang Educational Committee (Y202147815).

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Authors and Affiliations

  1. China Jiliang University, Hangzhou, Zhejiang, 310018, China

    Boya Ke, Huijuan Lu, Cunqian You & Wenjie Zhu

  2. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, Hangzhou, Zhejiang, 310018, China

    Boya Ke, Huijuan Lu, Cunqian You & Wenjie Zhu

  3. Drore Information and Technology Co., Hangzhou, Zhejiang, 310018, China

    Huijuan Lu

  4. College of Biomedical Engineering and Instrumentation Science Zhejiang University, Hangzhou, Zhejiang, 310018, China

    Li **e

  5. Institute of Advanced Digital Technologies and Instrumentation Zhejiang Province, Hangzhou, Zhejiang, 310018, China

    Li **e

  6. Department of Electrical and Computer Engineering Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Yudong Yao

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  1. Boya Ke
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  2. Huijuan Lu
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  3. Cunqian You
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  4. Wenjie Zhu
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  5. Li **e
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Correspondence to Huijuan Lu or Cunqian You.

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Ke, B., Lu, H., You, C. et al. A semi-supervised medical image classification method based on combined pseudo-labeling and distance metric consistency. Multimed Tools Appl 83, 33313–33331 (2024). https://doi.org/10.1007/s11042-023-16383-w

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