To benefit the skin care, this paper aims to design an automatic and effective visual analysis framework, with the expectation of recognizing the skin disease from a given image conveying the disease affected surface. This task is nontrivial, since it is hard to collect sufficient well-labeled samples. To address such problem, we present a novel transfer learning model, which is able to incorporate external knowledge obtained from the rich and relevant Web images contributed by grassroots. In particular, we first construct a target domain by crawling a small set of images from vertical and professional dermatological websites. We then construct a source domain by collecting a large set of skin disease related images from commercial search engines. To reinforce the learning performance in the target domain, we initially build a learning model in the target domain, and then seamlessly leverage the training samples in the source domain to enhance this learning model. The distribution gap between these two domains are bridged by a linear combination of Gaussian kernels. Instead of training models with low-level features, we resort to deep models to learn the succinct, invariant, and high-level image representations. Different from previous efforts that focus on a few types of skin diseases with a small and confidential set of images generated from hospitals, this paper targets at thousands of commonly seen skin diseases with publicly accessible Web images. Hence the proposed model is easily repeatable by other researchers and extendable to other disease types. Extensive experiments on a real-world dataset have demonstrated the superiority of our proposed method over the state-of-the-art competitors.

中文翻译：

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在计算机视觉框架下探索Web图像以增强皮肤疾病分析

为了使皮肤护理受益，本文旨在设计一种自动有效的视觉分析框架，以期从传达受疾病影响的表面的给定图像中识别出皮肤疾病。由于很难收集足够的标记良好的样品，因此这项任务并非易事。为了解决这个问题，我们提出了一种新颖的转移学习模型，该模型能够整合从基层贡献的丰富而相关的Web图像中获得的外部知识。特别是，我们首先通过从垂直的专业皮肤病学网站爬取一小组图像来构建目标域。然后，我们通过从商业搜索引擎中收集大量与皮肤疾病相关的图像来构建源域。为了增强目标领域的学习效果，我们首先在目标域中构建学习模型，然后无缝地利用源域中的训练样本来增强此学习模型。这两个域之间的分布差距通过高斯核的线性组合得以弥合。代替训练具有低级特征的模型，我们求助于深度模型来学习简洁，不变和高级的图像表示。与以往针对少数几种皮肤病的努力不同，这些努力通过医院提供的一小部分机密图像来制作，而本文针对具有公开可访问Web图像的数千种常见皮肤病进行了研究。因此，提出的模型很容易被其他研究人员重复，并且可以扩展到其他疾病类型。