一个值得信赖的课程学习指导多目标域适应网络自闭症谱系障碍分类
IEEE journal of biomedical and health informatics
|October 8, 2024
概括
这项研究介绍了一种新的可靠的课程学习指导多目标域适应 (TCL-MTDA) 网络,用于在多个网站上识别自闭症谱系障碍 (ASD). 该方法显著提高了多目标ASD分类的准确性,解决了现有的域调整技术的局限性.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 医疗成像医学成像
背景情况:
- 自闭症谱系障碍 (ASD) 诊断通常依赖于多中心数据,从而提出领域转移的挑战.
- 现有的领域适应方法通常集中在单个目标分类上,限制了它们在多地点ASD识别中的临床适用性.
- 在各种数据集中准确地识别ASD对于更广泛的临床采用至关重要.
研究的目的:
- 开发一个新的可信的课程学习指导多目标域适应 (TCL-MTDA) 网络.
- 为应对同时在多个目标领域识别ASD的挑战.
- 提高ASD分类在多中心环境中的稳定性和准确性.
主要方法:
- 提出了一个值得信赖的课程学习指导多目标域适应 (TCL-MTDA) 网络.
- 根据Dempster-Shafer (D-S) 证据理论,可靠的课程学习可以处理不同的数据转移.
- 集成了一个域-对比的适应方法来调整源和目标域分布,并学习域不变特征.
主要成果:
- 在ABIDE数据集上的四个目标域中,TCL-MTDA网络实现了71.46%的平均准确率.
- 拟议的方法在多目标ASD分类中显著优于大多数基线方法 (p<0.05).
- 在处理多个临床场所的数据异质性方面表现出有效性,用于ASD识别.
结论:
- 在多目标,多中心场景中,TCL-MTDA网络为强大的ASD识别提供了一个有前途的解决方案.
- 整合值得信赖的课程学习和域-对比的适应提高了分类性能.
- 这种方法提升了将机器学习模型应用于各种临床ASD数据集的潜力.
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