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.

本研究解决了医学影像模型与传统非影像和表格数据比较的挑战，提出了一种可解释的框架Barttender，通过将非影像特征转化为灰度条，便于进行深度学习建模。实验结果显示，Barttender在胸部X光片与健康记录数据集上的表现与传统方法相当，同时在可解释性方面大幅提升。

Barttender：一种易于理解和解释的比较医学影像与非影像数据的方法