用于基于皮肤镜的非侵入性数字系统自动分析的视觉Transformer皮肤癌分割与分类
Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System
AI总结：
本研究提出基于Vision Transformer的皮肤癌分类与Segment Anything Model分割方法，在HAM10000数据集上ViT patch-32变体达96.15%准确率，为皮肤科诊断提供有效工具。
摘要：
皮肤癌是全球关注的健康问题，需要早期准确诊断以改善患者预后。本研究引入了一种开创性的皮肤癌分类方法，采用Vision Transformer（视觉Transformer），这是一种因在多种图像分析任务中取得成功而闻名的最先进深度学习架构。利用包含10,015张精心标注的皮肤病变图像的HAM10000数据集，模型经过预处理以增强鲁棒性。适应于皮肤癌分类任务的Vision Transformer利用自注意力机制捕获复杂的空间依赖性，实现了优于传统深度学习架构的性能。Segment Anything Model辅助对癌变区域进行精确分割，获得了高IOU和Dice Coefficient。大量实验突显了该模型的优越性，特别是基于Google的ViT patch-32变体，其准确率达到96.15%，并展现出作为皮肤科医生皮肤癌诊断有效工具的潜力，为皮肤科实践的进步做出贡献。
英文摘要：
Skin cancer is a global health concern, necessitating early and accurate diagnosis for improved patient outcomes. This study introduces a groundbreaking approach to skin cancer classification, employing the Vision Transformer, a state-of-the-art deep learning architecture renowned for its success in diverse image analysis tasks. Utilizing the HAM10000 dataset of 10,015 meticulously annotated skin lesion images, the model undergoes preprocessing for enhanced robustness. The Vision Transformer, adapted to the skin cancer classification task, leverages the self-attention mechanism to capture intricate spatial dependencies, achieving superior performance over traditional deep learning architectures. Segment Anything Model aids in precise segmentation of cancerous areas, attaining high IOU and Dice Coefficient. Extensive experiments highlight the model's supremacy, particularly the Google-based ViT patch-32 variant, which achieves 96.15% accuracy and showcases potential as an effective tool for dermatologists in skin cancer diagnosis, contributing to advancements in dermatological practices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
