Deep learning is a branch of artificial intelligence that uses computational networks inspired by the human brain to extract patterns from raw data. Development and application of deep learning methods for image analysis, including classification, segmentation, and restoration, have accelerated in the last decade. These tools have been progressively incorporated into several research fields, opening new avenues in the analysis of biomedical imaging. Recently, the application of deep learning to dermatological images has shown great potential. Deep learning algorithms have shown performance comparable with humans in classifying skin lesion images into different skin cancer categories. The potential relevance of deep learning to the clinical realm created the need for researchers in disciplines other than computer science to understand its fundamentals. In this paper, we introduce the basics of a deep learning architecture for image classification, the convolutional neural network, in a manner accessible to nonexperts. We explain its fundamental operation, the convolution, and describe the metrics for the evaluation of its performance. These concepts are important to interpret and evaluate scientific publications involving these tools. We also present examples of recent applications for dermatology. We further discuss the capabilities and limitations of these artificial intelligence-based methods.

中文翻译：

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研究技术变得简单：皮肤图像分类的深度学习。

深度学习是人工智能的一个分支，它使用受人脑启发的计算网络从原始数据中提取模式。在过去的十年中，用于图像分析的深度学习方法（包括分类，分割和还原）的开发和应用已经加速。这些工具已逐步纳入几个研究领域，为生物医学成像分析开辟了新途径。最近，深度学习在皮肤病学图像中的应用已显示出巨大的潜力。在将皮肤病变图像分类为不同的皮肤癌类别中，深度学习算法已显示出与人类相当的性能。深度学习与临床领域的潜在相关性使计算机科学以外的其他学科的研究人员需要了解其基础知识。在本文中，我们以非专家可以访问的方式介绍了用于图像分类的深度学习架构的基础，即卷积神经网络。我们将解释其基本操作，卷积，并描述用于评估其性能的指标。这些概念对于解释和评估涉及这些工具的科学出版物非常重要。我们还介绍了皮肤病学的最新应用实例。我们将进一步讨论这些基于人工智能的方法的功能和局限性。以非专家可以访问的方式。我们将解释其基本操作，卷积，并描述用于评估其性能的指标。这些概念对于解释和评估涉及这些工具的科学出版物非常重要。我们还介绍了皮肤病学的最新应用实例。我们将进一步讨论这些基于人工智能的方法的功能和局限性。以非专家可以访问的方式。我们将解释其基本操作，卷积，并描述用于评估其性能的指标。这些概念对于解释和评估涉及这些工具的科学出版物非常重要。我们还介绍了皮肤病学的最新应用实例。我们将进一步讨论这些基于人工智能的方法的功能和局限性。