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

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 semisupervised 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.