Skin cancer is a prevalent and potentially fatal disease that requires accurate and efficient diagnosis and treatment. Although manual tracing is the current standard in clinics, automated tools are desired to reduce human labor and improve accuracy. However, developing such tools is challenging due to the highly variable appearance of skin cancers and complex objects in the background. In this paper, we present SkinSAM, a fine-tuned model based on the Segment Anything Model that showed outstanding segmentation performance. The models are validated on HAM10000 dataset which includes 10015 dermatoscopic images. While larger models (ViT_L, ViT_H) performed better than the smaller one (ViT_b), the finetuned model (ViT_b_finetuned) exhibited the greatest improvement, with a Mean pixel accuracy of 0.945, Mean dice score of 0.8879, and Mean IoU score of 0.7843. Among the lesion types, vascular lesions showed the best segmentation results. Our research demonstrates the great potential of adapting SAM to medical image segmentation tasks.

翻译：皮肤癌是一种常见且可能致命的疾病，需要准确高效的诊断与治疗。尽管手动描摹是当前临床标准，但自动化工具的研发仍需以减少人力投入并提升精度。然而，由于皮肤癌外观高度多变且背景中存在复杂物体，开发此类工具颇具挑战性。本文提出的SkinSAM是基于Segment Anything模型微调的分割模型，展现出卓越的分割性能。该模型在包含10015张皮肤镜图像的HAM10000数据集上进行了验证。虽然较大模型（ViT_L、ViT_H）性能优于较小模型（ViT_b），但微调模型（ViT_b_finetuned）提升最为显著，其平均像素精度达0.945，平均Dice分数达0.8879，平均IoU分数达0.7843。在病变类型中，血管病变的分割效果最佳。本研究充分展示了将SAM应用于医学图像分割任务的巨大潜力。