Table_1_Identifying the role of vision transformer for skin cancer—A scoping review.DOCX

IntroductionDetecting and accurately diagnosing early melanocytic lesions is challenging due to extensive intra- and inter-observer variabilities. Dermoscopy images are widely used to identify and study skin cancer, but the blurred boundaries between lesions and besieging tissues can lead to incorrect identification. Artificial Intelligence (AI) models, including vision transformers, have been proposed as a solution, but variations in symptoms and underlying effects hinder their performance.
ObjectiveThis scoping review synthesizes and analyzes the literature that uses vision transformers for skin lesion detection.
MethodsThe review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Revise) guidelines. The review searched online repositories such as IEEE Xplore, Scopus, Google Scholar, and PubMed to retrieve relevant articles. After screening and pre-processing, 28 studies that fulfilled the inclusion criteria were included.
Results and discussionsThe review found that the use of vision transformers for skin cancer detection has rapidly increased from 2020 to 2022 and has shown outstanding performance for skin cancer detection using dermoscopy images. Along with highlighting intrinsic visual ambiguities, irregular skin lesion shapes, and many other unwanted challenges, the review also discusses the key problems that obfuscate the trustworthiness of vision transformers in skin cancer diagnosis. This review provides new insights for practitioners and researchers to understand the current state of knowledge in this specialized research domain and outlines the best segmentation techniques to identify accurate lesion boundaries and perform melanoma diagnosis. These findings will ultimately assist practitioners and researchers in making more authentic decisions promptly.

引言：由于黑素细胞病变存在广泛的观察者内与观察者间差异，检测并准确诊断早期黑素细胞病变极具挑战性。皮肤镜图像被广泛用于皮肤癌的识别与研究，但病变与周围组织间模糊的边界可能导致识别错误。包括视觉Transformer（Vision Transformer）在内的人工智能（AI）模型已被提出作为解决方案，但症状表现与潜在影响的差异会制约其性能。
研究目的：本范围综述对使用视觉Transformer进行皮肤病变检测的相关文献进行综合与分析。
研究方法：本综述遵循PRISMA-ScR（系统综述与元分析范围综述扩展首选报告条目，Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews）指南开展。研究检索了IEEE Xplore、Scopus、Google Scholar及PubMed等在线数据库以获取相关文献，经筛选与预处理后，最终纳入28项符合纳入标准的研究。
结果与讨论：本综述发现，2020年至2022年间，采用视觉Transformer开展皮肤癌检测的相关研究数量快速增长，且在皮肤镜图像的皮肤癌检测任务中展现出优异性能。综述不仅点明了内在视觉歧义、皮肤病变形状不规则等诸多棘手挑战，还探讨了阻碍视觉Transformer在皮肤癌诊断中可信度提升的关键问题。本综述为从业者与研究者理解该细分研究领域的当前知识状态提供了全新视角，并概述了可用于精准识别病变边界、开展黑色素瘤诊断的最优分割技术。上述研究成果最终将助力从业者与研究者更快做出更可靠的决策。