基础模型驱动的皮肤损伤细分和分类使用SAM适配器和视觉转换器
Faisal Binzagr1, Majed Hariri2
1Department of Computer Science, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University Rabigh, Jeddah 21589, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一种新的框架,用于自动化皮肤癌检测,使用诸如Segment Anything Model (SAM) 和Vision Transformers (ViTs) 等基础模型. 该方法提高了病变细分和分类准确性,以改善诊断支持.
科学领域:
- 医疗成像中的人工智能
- 计算皮肤病学 计算皮肤病学
- 机器学习用于医疗保健
背景情况:
- 自动化皮肤癌评估在细分和分类皮肤镜图像方面面临挑战,原因是病变的变异性,低对比度和工件.
- 像分段任何模型 (SAM) 这样的基础模型显示了泛化潜力,但需要针对医学成像进行域特定的适应.
- 视觉转换器 (ViT) 提供了强大的识别,但缺乏对于损伤分析至关重要的空间先验.
研究的目的:
- 开发一个基于基础模型的综合框架,用于增强皮肤镜像分析.
- 提高自动化皮肤病变细分和分类的准确性和可靠性.
- 创建一个以病变为中心的自动化分析系统,用于皮肤癌检测中的潜在临床决策支持.
主要方法:
- 使用SAM-Adapter-微调精确的病变细分,保持SAM编码器冷和微调轻量级适配器,以适应皮肤表面.
- 采用基于视觉转换器 (ViT) 的分类器,结合了从细分和交叉注意力融合中获得的病变特异性裁剪.
- 集成的细分先验与补丁嵌入病变为中心的推理和训练的管道使用ISIC 2018,HAM10000和PH2数据集的联合多任务方法.
主要成果:
- 拟议的SAM-ViT框架实现了最先进的性能,在细分和分类方面都超过了现有的方法.
- 在ISIC 2018数据集上实现了94.27%的Dice分数对细分和95.88%的准确性对分类.
- 在其他数据集上表现出高性能,PH2上的子得分为95.62%,HAM10000的准确性为96.37%,具有统计学上显著的改进 (p<0.01).
结论:
- 将基础模型细分与基于变压器的分类相结合,可显著提高损伤边界质量和诊断准确性.
- SAM-ViT框架为自动化皮肤镜分析提供了强大的,可泛化和以病变为中心的方法.
- 这代表了向临床部署的皮肤癌检测决策支持系统迈出的有希望的一步,未来的工作重点是模型压缩和现实世界的临床验证.
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