你看的是什么? 在多式联络医疗深度学习中的模式贡献
Christian Gapp1,2, Elias Tappeiner3, Martin Welk3
1Institute of Biomedical Image Analysis, UMIT TIROL - Private University for Health Sciences and Health Technology, Eduard-Wallnöfer-Zentrum 1, 6060, Hall in Tirol, Austria. christian.gapp@umit-tirol.at.
International journal of computer assisted radiology and surgery
|October 2, 2025
概括
这项研究引入了一种新方法,以了解深度学习模型如何在医学中使用不同的数据类型 (模式). 它揭示了模型偏差和数据集不平衡,有助于多式联络人工智能开发以实现更好的临床整合.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 多模式数据分析在医学中至关重要,因为它提供了高维度的患者信息.
- 深度神经网络 (DNN) 越来越多地用于多式联网数据分析.
- 了解DNN如何处理来自单个数据源的信息仍然是一个挑战.
研究的目的:
- 开发和应用一种模型和性能无关的方法来量化多式模式深度学习中的模式贡献.
- 在多模式模型中调查单个来源的详细信息处理.
- 提高深度学习模型在多式联络医学研究中的可解释性.
主要方法:
- 实施了基于封闭的模式贡献方法.
- 量化测量每个数据模式对模型任务执行的重要性.
- 将该方法应用于三个不同的多式联络医疗数据集以进行验证.
主要成果:
- 确定了深度学习网络表现出模式偏好,导致单模式崩的实例.
- 揭示了某些多模式数据集内固有的不平衡.
- 为每个模式提供细粒度的定量和视觉属性重要性评估.
结论:
- 开发的指标为推进多式联运模型开发和数据集创建提供了有价值的见解.
- 这种可解释性方法有助于将多模式人工智能 (AI) 整合到临床实践中.
- 模式贡献方法的代码是公开可用的,以支持研究.
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