基于皮肤镜图像的基底细胞癌无活检亚型分类的深度学习
Deep Learning for Biopsy-Free Subtyping of Basal Cell Carcinoma from Dermatoscopic Images
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中文总结 AI 辅助
本文利用预训练视觉变换器，基于皮肤镜图像对基底细胞癌进行无活检亚型分类，在1271张图像上达到AUC 0.784，优于CNN基线和人类表现。
中文摘要 AI 辅助
基底细胞癌（BCC）是最常见的皮肤癌类型，约占皮肤癌诊断的近80%。其最佳临床管理由不同的组织病理学亚型指导，其中侵袭性变异需要更彻底的治疗措施。在当前临床实践中，亚型分类依赖于皮肤活检，这一过程既昂贵又具有侵入性。本文初步研究了仅利用病灶的单张皮肤镜图像，通过深度学习进行BCC亚型分类。鉴于我们可用的数据有限，我们采用了预训练的视觉变换器（ViTs），这是一类最先进的模型家族，在标注数据有限的情况下对具有挑战性的下游任务非常有效。通过重复分层k折交叉验证，我们证明在区分侵袭性BCC与其他亚型家族的任务中，ViTs能够达到优于标准CNN基线和先前报道的人类读者表现的性能（在包含1271张各种BCC亚型皮肤镜图像的数据集上AUC为0.784）。这些初步发现凸显了将深度学习与皮肤镜检查相结合，为BCC亚型分类提供无活检替代方案的潜力，从而有助于改善治疗计划和患者预后。
英文摘要
Basal Cell Carcinoma (BCC) is the most common type of skin cancer, accounting for nearly 80% of skin cancer di- agnoses. Its optimal clinical management is guided by the distinct histopathologic subtype, with aggressive variants requiring more drastic measures. In current clinical practice, subtyping relies on skin biopsies, a procedure both costly and invasive. In this paper, we conduct a preliminary investigation into using deep learning for BCC subtyping, solely from a single dermatoscopic image of the lesion. Given the limited data at our disposal, we employ pre-trained vision transformers (ViTs), a state-of-the-art family of models highly effective for challenging downstream tasks with limited labeled data. Through repeated stratified k-fold cross-validation, we demonstrate that ViTs can achieve superior performance (AUC 0.784 on a dataset of 1271 dermatoscopic images of various BCC subtypes) over standard CNN-based baselines as well as previously-reported human reader perfor- mance, on the task of differentiating aggressive BCCs from other subtype families. These initial findings highlight the potential of combining deep learning and dermatoscopy to provide a biopsy- free alternative for BCC subtyping, thus aiding in improving treatment planning and patient outcomes.
