IEEE Trans Biomed Eng. 2019 Apr;66(4):1006-1016. doi: 10.1109/TBME.2018.2866166. Epub 2018 Aug 20.
In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very deep residual neural network pretrained on a large natural image dataset. Then these local deep descriptors are aggregated by orderless visual statistic features based on Fisher vector (FV) encoding to build a global image representation. Finally, the FV encoded representations are used to classify melanoma images using a support vector machine with a Chi-squared kernel. Our proposed method is capable of generating more discriminative features to deal with large variations within melanoma classes, as well as small variations between melanoma and nonmelanoma classes with limited training data. Extensive experiments are performed to demonstrate the effectiveness of our proposed method. Comparisons with state-of-the-art methods show the superiority of our method using the publicly available ISBI 2016 Skin lesion challenge dataset.
在本文中，我们提出了一种通过深度学习方法和局部描述符编码策略进行皮肤镜图像识别的新框架。具体来说，通过在大型自然图像数据集上预训练的非常深的残差神经网络，首先提取缩放后的皮肤镜图像的深度表示。然后，通过基于 Fisher 向量（FV）编码的无序视觉统计特征来聚合这些局部深度描述符，以构建全局图像表示。最后，使用带有卡方核的支持向量机对 FV 编码的表示进行分类，以对黑色素瘤图像进行分类。我们提出的方法能够生成更具判别力的特征，以处理黑色素瘤类别内的大变化，以及黑色素瘤和非黑色素瘤类别之间的小变化，同时使用有限的训练数据。进行了广泛的实验以证明我们提出的方法的有效性。与最先进的方法进行比较，使用公开的 ISBI 2016 皮肤病变挑战数据集显示了我们方法的优越性。
