让影像与非影像数据直接比高低，还能看懂原因。
Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data
- 把电子病历数据转成灰度条，统一用深度学习比较
- 在CheXpert和MIMIC上表现不输传统方法，解释性更强
- 新增gIoU指标，可分析图像模型的全局重要特征
基于影像的深度学习已推动医疗研究发展，但其临床应用受限于影像模型与传统非影像、表格数据之间的比较困难。为此，我们提出Barttender，一种可解释的框架，通过深度学习直接比较影像与非影像表格数据在疾病预测等任务中的实用性。Barttender将电子健康记录中的标量数据转化为灰度条，实现两类数据模态的可解释、可扩展建模。该框架支持通过性能指标以及局部（样本级）和全局（人群级）解释来评估二者效用差异。我们引入一种新指标gIoU，用于定义图像模型的全局特征重要性。在包含胸部X光片和电子病历标量数据的CheXpert与MIMIC数据集上的实验表明，Barttender性能媲美传统方法，并通过深度学习模型提供更强可解释性。
原文摘要 · Abstract (English)
Imaging-based deep learning has transformed healthcare research, yet its clinical adoption remains limited due to challenges in comparing imaging models with traditional non-imaging and tabular data. To bridge this gap, we introduce Barttender, an interpretable framework that uses deep learning for the direct comparison of the utility of imaging versus non-imaging tabular data for tasks like disease prediction. Barttender converts non-imaging tabular features, such as scalar data from electronic health records, into grayscale bars, facilitating an interpretable and scalable deep learning based modeling of both data modalities. Our framework allows researchers to evaluate differences in utility through performance measures, as well as local (sample-level) and global (population-level) explanations. We introduce a novel measure to define global feature importances for image-based deep learning models, which we call gIoU. Experiments on the CheXpert and MIMIC datasets with chest X-rays and scalar data from electronic health records show that Barttender performs comparably to traditional methods and offers enhanced explainability using deep learning models.
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