通过自我监督学习改善心脏MRI中的冠状动脉疾病诊断
Usman Khalid1, Mehmet Kaya1, Reda Alhajj2,3,4
1Department of Computer Engineering, Firat University, Elazig 23200, Turkey.
Diagnostics (Basel, Switzerland)
|October 29, 2025
概括
使用借口任务的自主监督学习 (SSL) 显示出冠状动脉疾病 (CAD) 从心脏MRI分类的前景,特别是在有限的数据的情况下. 一个高斯式噪声借口任务实现了高精度和强度对各种挑战.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的机器学习
- 用于心血管疾病的人工智能
背景情况:
- 医疗图像分类的监督学习受限于需要广泛的标记数据,这是昂贵和耗时的,特别是在医疗保健中.
- 自主监督学习 (SSL) 提供了一个解决方案,通过借口任务利用未标记的数据,减少对手工注释的依赖.
- 这项研究调查了SSL对冠状动脉疾病 (CAD) 通过心脏MRI进行分类的有效性,解决了数据稀缺性和稳定性等挑战.
研究的目的:
- 用心脏MRI数据评估监督和无监督自主监督学习 (SSL) 借口模型对冠状动脉疾病 (CAD) 分类的有效性.
- 在数据稀缺,分布外条件和对抗性攻击下评估模型性能.
- 将SSL借口模型与对比学习框架 (SimCLR) 进行比较.
主要方法:
- 两个心脏MRI数据集 (CAD心脏MRI和OCMR) 用于训练三个借口任务:监督的高斯噪声加法,监督的图像旋转和无监督的生成重建.
- 模型在不同的数据减少场景 (20%,50%,70%),分布外条件和对抗性攻击 (FGSM,PGD) 下进行了评估.
- 绩效与对比学习的简单框架 (SimCLR) 进行了比较.
主要成果:
- 监督的高斯噪声借口模型在所有数据减少场景中实现了最高的验证准确性 (高达99.9%),并证明了对抗对手攻击的优越稳定性.
- 基于轮换的模型显示了对攻击的显著易感性,并且在较少的数据下降了准确性.
- 生成式重建模型的性能适度,准确性损失最小. 在标准条件下,SimCLR是强大的,但不如高斯噪声模型.
结论:
- 精心设计的自我监督的借口任务可以在心脏MRI分类中实现可靠的性能和通用性,即使数据有限.
- 在安全关键的医疗保健应用中,SSL显示了开发强大的模型的潜力.
- 建议在各种数据集和临床环境中进行进一步验证,以充分实现SSL的功能.
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