With the rapid increase in the cases of deadly skin cancer, the classification on different types of skin cancer has been emerging as one of the most significant issues in the field of medical image. Several approaches have been proposed to help in diagnosing the categories of the skin lesions by means of traditional features or leveraging the widely used deep learning models. However, there are lack of the integrated frameworks to combine the hand-crafted traditional features and the deep Conv-features. Furthermore, the effective way to extract global and local features is also conducive to distinguish the specific lesions from normal skin. Hence, in this study, the authors present an integrated model to acquire more representative global–local features including the traditional local binary pattern features and deep Conv-features. In addition, several fusion strategies have conducted on the Global-DNN and Local-DNN for better performance. In order to extract more explicit features from the specific lesion areas, a target segmentation method based on visual saliency detection is employed to eliminate the background interference. Experimental results on ISIC-2017 skin cancer dataset demonstrate that the proposed Global-DNN and Global-Local models can obtain more effective feature representation which achieve outperformed results for skin cancer classification.

中文翻译：

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基于视觉显着性的皮肤癌分类的全局-局部特征表示

随着致命性皮肤癌病例的迅速增加，对不同类型皮肤癌的分类已成为医学图像领域最重要的问题之一。已经提出了几种方法来帮助通过传统特征或利用广泛使用的深度学习模型来诊断皮肤病变的类别。但是，缺乏将手工制作的传统功能与深层的Conv功能相结合的集成框架。此外，提取全局和局部特征的有效方法也有利于区分正常皮肤和特定病变。因此，在这项研究中，作者提出了一个集成模型来获取更具代表性的全局-局部特征，包括传统的局部二进制模式特征和深层的Conv特征。此外，为了更好的性能，在Global-DNN和Local-DNN上进行了几种融合策略。为了从特定病变区域中提取更明确的特征，采用了基于视觉显着性检测的目标分割方法来消除背景干扰。在ISIC-2017皮肤癌数据集上的实验结果表明，所提出的Global-DNN和Global-Local模型可以获取更有效的特征表示，从而获得优于皮肤癌分类的结果。