跨域不变特征吸收和域特定特征保留,用于域增量胸部X射线分类
IEEE transactions on medical imaging
|March 3, 2025
概括
这项研究引入了一个名为CaD的新框架,通过防止灾难性遗忘来改善域增量学习 (DIL) 中的胸部X射线 (CXR) 分类. 电子诊断框架有效地平衡了新信息的学习,同时保留了先前的知识,以提高诊断准确度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 胸部X射线 (CXR) 分类模型对于临床诊断至关重要.
- 静态培训方法在现实临床环境中与持续学习作斗争,导致域增量学习 (DIL) 挑战.
- 灾难性遗忘是DIL中的一个重大障碍,原因是大范围的域间隙.
研究的目的:
- 提出一个新的框架,跨域不变特征吸收和域特定特征保留 (CaD),以解决CXR分类在DIL中的灾难性遗忘问题.
- 开发模块,学习领域不变的知识,并有效地保留领域特定的知识.
- 在培训期间,增强每个领域内的类别的独特性.
主要方法:
- 引入了一个跨域不变特征吸收 (CIFA) 模块,配备了一个C (Class) 适配器,以融合跨域的共同特征.
- 实现了一个域特定特征保留 (DSFR) 模块,每个域都有D ((domain) 适配器,并行连接以防止遗忘.
- 使用多标签对比损失 (MLCL) 来提高域内类别的区别性.
主要成果:
- 拟议的CaD框架在使用大规模公共数据集的模拟DIL场景中表现出有效性.
- 在CIFA和DSFR模块中,成功地平衡了学习新领域知识和保留现有知识.
- MLCL提高了每个域内的特征的区分能力.
结论:
- 在CXR分类中,CaD框架为域增量学习提供了一个强大的解决方案,减轻了灾难性遗忘.
- 拟议的方法实现了最先进的性能,在DIL设置中表现优于现有的方法.
- 这项工作促进了AI在医学成像中的应用,通过从各种数据领域实现持续学习.
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