提高皮肤疾病分类,利用基于变压器的深度学习架构和可解释的AI来提高皮肤疾病的分类
Jayanth Mohan1, Arrun Sivasubramanian1, Sowmya V1
1Amrita School of Artificial Intelligence, Coimbatore, Amrita Vishwa Vidyapeetham, India.
Computers in biology and medicine
|March 21, 2025
概括
新的人工智能模型DinoV2显著提高了皮肤疾病分类的准确性. 这一进步有助于皮肤科医生更有效地诊断和计划治疗.
科学领域:
- 皮肤病学 皮肤病学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 皮肤疾病影响全球人口的很大一部分.
- 准确及时诊断至关重要,但具有挑战性.
- 自动化皮肤病分类对于临床支持至关重要.
研究的目的:
- 评估变压器模型的有效性,特别是DinoV2,用于皮肤疾病的自动分类.
- 将DinoV2的性能与现有的基准和CNN架构进行比较.
- 使用可解释的人工智能技术增强模型的解释性.
主要方法:
- 使用视觉变压器,Swin变压器,并在31类皮肤疾病数据集上引入DinoV2.
- 在HAM10000和Dermnet数据集上验证了模型的稳定性.
- 使用GradCAM和SHAP进行可解释的AI分析.
主要成果:
- 在31个类数据集上,DinoV2实现了96.48%的准确性和97.27%的F1-Score,实现了最先进的性能.
- 与现有基准相比,表现出显著的改善 (近10%).
- 在HAM10000和Dermnet数据集上始终优于先前的模型.
结论:
- 变压器模型,特别是DinoV2,显示出皮肤病应用的巨大潜力.
- 可解释的AI技术为诊断和治疗规划提供了宝贵的临床见解.
- 这种方法提高了透明度,并支持皮肤病学中的临床决策.
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