批量平衡焦点损失:在深度学习中解决类不平衡的混合解决方案
Jatin Singh1, Cameron Beeche1, Zhiyi Shi1
1University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States.
Journal of medical imaging (Bellingham, Wash.)
|June 26, 2023
概括
批量平衡焦点损失 (BBFL) 改善了卷积神经网络 (CNN) 在不平衡的医疗数据集上的性能. 这种方法提高了对格洛科马和视网膜神经纤维层缺陷等疾病的分类准确性.
科学领域:
- 医学成像分析 医学成像分析
- 机器学习用于医疗保健
- 在眼科中使用计算机视觉.
背景情况:
- 医学数据集中的阶级不平衡对开发准确的诊断模型构成重大挑战.
- 卷积神经网络 (CNN) 是图像分类的强大工具,但与不平衡的数据作斗争.
- 处理不平衡数据的现有方法在复杂的医学成像任务中往往不足.
研究的目的:
- 为了验证一种新的方法的有效性,分批平衡焦点损失 (BBFL),用于提高CNN对不平衡数据集的分类性能.
- 评估BBFL在医疗图像分类任务中提高诊断准确性的能力.
主要方法:
- BBFL是通过结合批量平衡和焦点损失策略来开发的,以解决类不平衡和硬样本重要性的问题.
- 这种方法在两个不平衡的底部图像数据集上得到了验证:视网膜神经纤维层缺陷 (RNFLD) 和青光眼.
- BBFL与随机过量抽样 (ROS),成本敏感学习和使用最先进的CNN (InceptionV3,MobileNetV2) 的值进行了比较.
主要成果:
- 对于二进制RNFLD分类,BBFL获得了93.0%的准确率,84.7%的F1得分和0.971的AUC,优于其他方法.
- 在多类眼症分类中,BBFL获得了79.7%的准确率和69.6%的平均F1分数,超过了ROS,成本敏感学习和随机下样本.
- 使用混矩阵,t-SNE图和GradCAM进行的视觉评估证实了BBFL的卓越性能.
结论:
- 基于BBFL的学习方法显著提高了CNN模型在二进制和多类疾病分类任务中的性能,这些任务具有不平衡的数据.
- 在数据不平衡普遍存在的医学成像中,BBFL提供了一个强大的解决方案,以提高诊断准确度.
- 这种方法对开发更可靠的人工智能驱动的诊断工具在眼科和其他医疗领域具有前景.
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