在临床深度学习模型中提高可解释性:潜空间变量解码优于梯度加权类激活映射
Richard T Carrick1, Ethan J Rowin2, Alessio Gasperetti1
1Johns Hopkins Hypertrophic Cardiomyopathy Center, Division of Cardiology, Department of Medicine, Johns Hopkins University, Balitmore, Maryland.
Heart rhythm O2
|November 24, 2025
概括
隐形空间变量解码 (LSVD) 与梯度加权类激活映射 (Grad-CAM) 相比,为心脏病学中的深度学习模型提供了更好的解释性. LSVD提供了对决策过程的更清晰的见解,有助于临床决策支持.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 深度学习模型在心脏病学中越来越多地用于临床决策支持.
- 这是一个很棒的节目,这是一个很棒的节目.
- 黑盒子是一个黑盒子.
- 这些模型的性质阻碍了医生的信任和验证.
- 像Grad-CAM这样的可解释性技术缺乏对可靠性和可重现性的严格评估.
研究的目的:
- 严格评估心脏病学中Grad-CAM的解释性.
- 将Grad-CAM与来自内在可解释的深度学习模型的替代突出方法进行比较.
主要方法:
- 分析了1930年的一组超性心肌病 (HCM) 患者的心电图数据.
- 开发了新的深度学习模型,以预测左心室垂动脉瘤和大 LV 缩.
- 使用Grad-CAM和潜空间变量解码 (LSVD) 进行了度分析.
主要成果:
- 深度学习模型显示了两种技术的可比预测性能 (例如,C-统计学0.95对LV瘤0.93).
- 格拉德-CAM产生了可变的注意力地图,对决策的洞察力有限.
- 通过LSVD,可以直接可视化心电图学特征的差异化,并评估模型过拟合风险.
结论:
- 对于心脏病学中的深度学习模型,LSVD提供了比Grad-CAM更强大的可解释性.
- LSVD有助于更好地理解模型预测,提高临床效用.
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