Abstract
Skin lesion classification in the dermoscopy images exerts an enormous function on the improvement of diagnostic performance and reduction of melanoma deaths. This skin lesion classification task remains a challenge. Deep learning requires a lot of training data, and the classification algorithms of skin lesions have certain limitations. These two points make the accuracy of the skin lesion classification needs to be further improved. In this paper, a mutual learning model was presented to separate malignant from benign skin lesions using the skin dataset. This model enabled dual deep convolutional neural networks to mutually learn from each other. Experimental results on the ISIC 2016 Skin Lesion Classification dataset indicate that the mutual learning model obtains the most advanced performance.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 61672181, No. 51679058, Natural Science Foundation of Heilongjiang Province under Grant No. F2016005. We would like to thank our teacher for guiding this paper. We would also like to thank classmates for their encouragement and help. We acknowledged the International Skin Imaging Collaboration (ISIC) for the publication of the ISIC 2016 Skin Lesion Classification Dataset. In the meantime, We would like to thank the scholars cited in this paper for their support and answers.
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Wang, Y., Pan, H., Yang, B., Bian, X., Cui, Q. (2019). Mutual Learning Model for Skin Lesion Classification. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_17
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