通过弱到强的概括来提高手工制作的特征导向组织学图像分类的深度学习可解释性.
Zong Fan1, Changjie Lu1, Jialin Yue2
1University of Illinois Urbana-Champaign, Department of Bioengineering, Urbana, Illinois, United States.
Journal of medical imaging (Bellingham, Wash.)
|January 22, 2026
概括
本研究引入了一个弱到强的概括 (WSG) 框架,以改进用于组织学图像分析的深度学习 (DL) 模型. 整合手工制作的特征 (HCF) 提高了DL模型的解释性和预测性,用于临床采用.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能
- 图像分析 图像分析
背景情况:
- 深度学习 (DL) 模型在组织学图像分析方面表现出色,但缺乏解释性.
- 手工制作的特征 (HCF) 提供可解释性,但具有较低的预测能力.
- DL和HCF之间的关系未得到充分研究,阻碍了临床采用.
研究的目的:
- 为了提高DL模型的解释性和性能在组织学图像分析.
- 将HCF集成到DL模型中,使用弱至强泛化 (WSG) 框架.
- 为了更好的临床采用,探索DL和HCF之间的相关性.
主要方法:
- 开发了一个WSG框架,一个基于HCF的可解释的"弱"教师模型监督一个"强"DL学生模型.
- 设计了一个自适应式启动WSG丢失函数,以优化从HCF到DL功能的知识传输.
- 分析了HCF和DL特征之间的相互信息 (MI),以评估可解释性和相关性.
主要成果:
- WSG框架在各种模型中始终改善了分类性能.
- Saliency-map分析显示,WSG监督改善了对诊断相关区域的模型重点.
- 定量分析显示,在WSG培训后的HCF和DL特征之间增加了MI.
结论:
- WSG框架有效地将HCF整合到DL培训中,提高可解释性和预测性.
- 在组织学图像分类中阐明了驱动DL预测的关键HCF.
- 这些发现支持在病理学中更广泛地采用可解释的DL模型.
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