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Medical Big Data Analysis with Attention and Large Margin Loss Model for Skin Lesion Application

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Abstract

Due to melanoma is one of the skin cancers with the highest mortality rate and have a large amount of data during the collection and diagnosis, there is an urgent need to improve the diagnostic efficiency and accuracy. However, there remain problems in analyzing medical big data for skin lesion application, such as the intra-class variation and inter-class similarity in skin lesion images and the lacks of ability to focus on the lesion area affecting the classification results of the model. To address these dilemmas, in this paper, we proposed a novel machine learning-based approach that builds on top of DenseNet. It combines the attention mechanism and large margin loss to enhance the classification accuracy in terms of intra-class compactness and inter-class separability. We evaluated our model on ISIC 2017 (International Skin Imaging Collaboration) dataset, which has achieved 92% of Mean AUC. The experimental results show the effectiveness of our solution outperforms the state-of-the-art significantly in classify skin lesion and can accurately classify malignant melanoma on medical images.

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Authors and Affiliations

  1. Wuhan University of Science and Technology, Wuhan, China

    Jing Wu, Hong Guo, Wei Hu, YiNing Li, TianYi Liu & XiaoMing Liu

  2. Trinity College Dublin, Dublin, Ireland

    Yuan Wen

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  1. Jing Wu
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  2. Hong Guo
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  3. Yuan Wen
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  4. Wei Hu
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Correspondence to Hong Guo.

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Wu, J., Guo, H., Wen, Y. et al. Medical Big Data Analysis with Attention and Large Margin Loss Model for Skin Lesion Application. J Sign Process Syst 93, 827–839 (2021). https://doi.org/10.1007/s11265-021-01664-0

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