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在不平衡的医疗数据集上用于少数群体检测的等级级别对比学习

Yiyue Li, Guangwu Qian, Xiaoshuang Jiang

IEEE transactions on medical imaging

|August 31, 2023

概括

本研究介绍了层次实例对比学习 (HCLe),这是一种用于检测医学成像中罕见疾病的新型深度学习方法. 通过对多数类数据的培训,HCLe有效地解决了数据不平衡和标签不足的问题,优于现有的方法.

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:

深度学习模型面临着不平衡的数据集和有限的数据所带来的挑战,特别是在医学成像中,罕见疾病代表了分布多样化的少数群体.
标签不足和未见病例进一步使少数群体在医疗数据集中的培训变得复杂.

研究的目的:

提出一种新的等级实例对比学习 (HCLe) 方法,用于在医学成像中有效地检测少数群体.
通过仅在多数类数据上进行培训来解决数据不平衡和数据饥饿问题.

主要方法:

HCLe采用双分支架构:用于图像级特征提取的自动编码器具有约束函数,以及用于层次样本特征一致性的对比学习网络.
一个多样化的小批量战略完善了在各种条件下识别少数群体的方法.

主要成果:

拟议的HCLe方法在三个不同的医疗数据集 (不同的疾病和模式) 上进行了评估.
实验结果表明,HCLe在少数阶级检测方面明显优于当前最先进的方法.

结论:

在医学成像中,HCLe为少数群体检测提供了强大的解决方案,有效地克服了常见的深度学习限制.
该方法对改善医疗数据集中罕见疾病和其他少数群体类别的诊断具有前景.

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