The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis.

翻译：计算机辅助诊断或检测（CAD）方法用于皮肤病变分析是一个新兴的研究领域，有潜力减轻皮肤癌筛查的负担和成本。近年来，研究人员对开发此类CAD系统表现出日益浓厚的兴趣，旨在为皮肤科医生提供一种用户友好的工具，以减少手动检查所面临的挑战或相关问题。本文旨在对2011年至2022年间发表的共计594篇文献（其中356篇涉及皮肤病变分割，238篇涉及皮肤病变分类）进行全面的文献综述与回顾。这些文章通过多种不同方式进行分析和总结，以提供关于CAD系统开发方法的重要信息。这些方式包括相关且必要的定义与理论、输入数据（数据集利用、预处理、数据增强及不平衡问题修复）、方法配置（技术、架构、模块框架及损失函数）、训练策略（超参数设置）以及评估标准。我们计划研究多种性能提升方法，包括集成学习与后处理。我们还基于使用频率对这些维度进行探讨，以揭示其当前趋势。此外，我们重点指出了在利用小规模数据集评估皮肤病变分割与分类系统时面临的主要困难，以及应对这些困难的潜在解决方案。本文公开了研究发现、建议及趋势，以指导未来研究开发用于皮肤病变分析的自动化且稳健的CAD系统。