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Mixed Re-Sampled Class-Imbalanced Semi-Supervised Learning for Skin Lesion Classification

Ye Tian1, Liguo Zhang1,2, Linshan Shen1,*, Guisheng Yin1, Lei Chen3

1 Harbin Engineering University, College of Computer Science and Technology, Harbin, 150001, China
2 Heilongjiang Hengxun Technology Co., Ltd., Harbin, 150001, China
3 College of Engineering and Information Technology, Georgia Southern University, Statesboro, GA, 30458, USA

* Corresponding Author: Linshan Shen. Email: email

Intelligent Automation & Soft Computing 2021, 28(1), 195-211. https://doi.org/10.32604/iasc.2021.016314

Abstract

Skin cancer is one of the most common types of cancer in the world, melanoma is considered to be the deadliest type among other skin cancers. Quite recently, automated skin lesion classification in dermoscopy images has become a hot and challenging research topic due to its essential way to improve diagnostic performance, thus reducing melanoma deaths. Convolution Neural Networks (CNNs) are at the heart of this promising performance among a variety of supervised classification techniques. However, these successes rely heavily on large amounts of class-balanced clearly labeled samples, which are expensive to obtain for skin lesion classification in the real world. To address this issue, we propose a mixed re-sampled (MRS) class-imbalanced semi-supervised learning method for skin lesion classification, which consists of two phases, re-sampling, and multiple mixing methods. To counter class imbalance problems, a re-sampling method for semi-supervised learning is proposed, and focal loss is introduced to the semi-supervised learning to improve the classification performance. To make full use of unlabeled data to improve classification performance, Fmix and Mixup are used to mix labeled data with the pseudo-labeled unlabeled data. Experiments are conducted to demonstrate the effectiveness of the proposed method on class-imbalanced datasets, the results show the effectiveness of our method as compared with other state-of-the-art semi-supervised methods.

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APA Style
Tian, Y., Zhang, L., Shen, L., Yin, G., Chen, L. (2021). Mixed re-sampled class-imbalanced semi-supervised learning for skin lesion classification. Intelligent Automation & Soft Computing, 28(1), 195-211. https://doi.org/10.32604/iasc.2021.016314
Vancouver Style
Tian Y, Zhang L, Shen L, Yin G, Chen L. Mixed re-sampled class-imbalanced semi-supervised learning for skin lesion classification. Intell Automat Soft Comput . 2021;28(1):195-211 https://doi.org/10.32604/iasc.2021.016314
IEEE Style
Y. Tian, L. Zhang, L. Shen, G. Yin, and L. Chen "Mixed Re-Sampled Class-Imbalanced Semi-Supervised Learning for Skin Lesion Classification," Intell. Automat. Soft Comput. , vol. 28, no. 1, pp. 195-211. 2021. https://doi.org/10.32604/iasc.2021.016314

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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