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Boosting and rectifying few-shot learning prototype network for skin lesion classification based on the internet of medical things

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Abstract

The Internet of Medical Things (IoMT), with advances in wireless technologies, has boosted traditional healthcare into smart healthcare. Computer-aided diagnosis technology based on IoMT is thriving with the help of deep learning. However, fully supervised deep learning need to be trained with enough annotated samples, which is difficult in healthcare. Few-shot learning network can be trained with only a small number of annotated samples, which alleviates the difficulty of medical image collection and annotation. We proposed a few-shot prototype network to address the shortage of annotated samples based on IoMT. First, the capability of the feature extractor is enhanced by designing a contrast learning branch. Second, a novel strategy for constructing positive and negative sample pairs is proposed for the contrast learning, which avoids to specifically maintain a sample queue. Third, the contrast learning branch is also used to rectify the corruption samples and refine the category prototype. Finally, the hybrid loss, consisting of prototype loss and contrastive loss, is used to improve the classification accuracy and convergence speed. Our method achieved satisfactory performance on the mini-ISIC-2\(^i\) and mini-ImageNet datasets.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Junsheng Xiao, Huahu Xu, DiKai Fang, Chen Cheng & HongHao Gao

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  1. Junsheng Xiao
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  2. Huahu Xu
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  5. HongHao Gao
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Correspondence to HongHao Gao.

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Xiao, J., Xu, H., Fang, D. et al. Boosting and rectifying few-shot learning prototype network for skin lesion classification based on the internet of medical things. Wireless Netw 29, 1507–1521 (2023). https://doi.org/10.1007/s11276-021-02713-z

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