Skin cancer, also known as melanoma, is a deadly form of skin cancer that can significantly improve survival rates when diagnosed at an early stage. It is usually diagnosed visually from dermoscopic images, and such visual assessment of skin cancer by the naked eye is a challenging and arduous task. Therefore, the detection of melanoma from dermoscopic images using trained artificial intelligence models is of great importance today. However, since melanoma is a rare disease, existing databases of skin lesions often contain highly unbalanced numbers of benign and malignant samples. In this paper, we propose a new one-class classification-based skin lesion classification strategy for small and unbalanced datasets. One-class classification (OCC) is a special case of multi-classification. OCC aims to learn a descriptive paradigm from positive class data (true data) during training and reject pseudo data (fake data) that do not conform to the paradigm during inference. OCC has great potential for application in anomaly detection problems. We have analyzed several approaches to the OCC task in recent years and propose a new design paradigm for the OCC problem, taking into account the unbalanced data set of the melanoma classification task. We have designed an improved OCC network based on this design paradigm, where the network is based on the architecture of a residual neural network, combining the coding and decoding idea of variational self-encoder and the adversarial training idea of an adversarial neural network, using binary cross-entropy as the loss function and introducing the channel attention mechanism. Tests on several publicly available dermatology datasets show that this improved OCC network addresses the unbalanced dataset situation in melanoma image classification to some extent while having relatively excellent performance. Compared with some traditional networks, it can obtain more stable training results and perform more consistently on complex datasets.

中文翻译：

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残差神经网络辅助不平衡数据黑色素瘤识别的一类分类算法

皮肤癌，也称为黑色素瘤，是一种致命的皮肤癌，在早期诊断时可以显着提高生存率。通常通过皮肤镜图像进行视觉诊断，而通过肉眼对皮肤癌进行视觉评估是一项具有挑战性和艰巨的任务。因此，使用训练有素的人工智能模型从皮肤镜图像中检测黑色素瘤在今天非常重要。然而，由于黑色素瘤是一种罕见疾病，现有的皮肤病变数据库通常包含数量高度不平衡的良性和恶性样本。在本文中，我们针对小型且不平衡的数据集提出了一种新的基于一类分类的皮肤病变分类策略。一类分类（OCC）是多分类的一种特例。OCC的目标是在训练过程中从正类数据（真实数据）中学习描述范式，并在推理过程中拒绝不符合范式的伪数据（假数据）。OCC在异常检测问题中具有巨大的应用潜力。我们分析了近年来 OCC 任务的几种方法，并考虑到黑色素瘤分类任务的不平衡数据集，提出了 OCC 问题的新设计范式。基于该设计范式，我们设计了一种改进的OCC网络，该网络基于残差神经网络的架构，结合变分自编码器的编解码思想和对抗神经网络的对抗训练思想，使用二元交叉熵作为损失函数并引入通道注意机制。对几个公开的皮肤科数据集的测试表明，这种改进的OCC网络在一定程度上解决了黑色素瘤图像分类中数据集不平衡的情况，同时具有相对优异的性能。与一些传统网络相比，它可以获得更稳定的训练结果，并且在复杂数据集上表现更一致。