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MTAKD:多教师协议知识蒸,用于边缘AI皮肤病诊断

Andreas Winata¹, Nur Afny Catur Andryani², Alexander Agung Santoso Gunawan³

¹Computer Science Department, BINUS Graduate Program - Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia, 11480. andreas.winata001@binus.ac.id.

Scientific reports

|December 19, 2025

概括

这项研究引入了一个新的多教师知识蒸 (MTAKD) 框架,用于在移动设备上高效的边缘AI皮肤病诊断. MTAKD显著提高了模型的准确性和速度,克服了远程医疗机构的挑战.

科学领域:

人工智能的人工智能
医疗成像医学成像
计算机科学计算机科学

背景情况:

在偏远地区诊断皮肤疾病是很困难的,因为专家的访问有限,互联网也很差.
边缘人工智能 (AI) 可以实现设备上的处理,绕过云依赖.
为移动部署优化AI模型对于边缘计算至关重要.

研究的目的:

开发一种新的多教师知识蒸 (MTAKD) 框架,以提高移动边缘设备上的小型AI模型性能.
提高AI驱动的皮肤疾病诊断在资源有限的环境中的准确性和效率.
评估优化AI模型的可解释性和实际部署.

主要方法:

提出了一个多教师知识蒸 (MTAKD) 框架,利用动态的教师协议来权衡多个知识来源.
整合了三个新的蒸算法:协议加权,注意协议和关系协议知识蒸.
在使用ISIC 2019和Fitzpatrick17k-C数据集的移动原型上评估了学生模型的性能,可解释性和效率.

主要成果:

实现了高诊断准确率:在ISIC 2019上达到87.53%,在Fitzpatrick17k-C上达到44.75%,超过了基准.
证明了改进的模型可解释性,AUC分数为0.6796 (ISIC 2019) 和0.1724 (Fitzpatrick17k-C). 这是一个很好的例子.
实现了显著的效率提升:模型尺寸缩小了49.8倍,在移动原型上推断速度快了352倍.

结论:

该MTAKD框架是边缘AI皮肤疾病诊断的有效和实用解决方案.
MTAKD能够实现高性能的人工智能模型,适合在偏远地区部署在移动设备上.
这项研究解决了移动医疗人工智能的关键挑战,提高了可访问性和诊断能力.

关键词:

边缘AI 边缘AI 可解释的人工智能知识的蒸知识的蒸.模型的压缩压缩.皮肤疾病皮肤疾病

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