Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint
Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint
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DOI：
10.2139/ssrn.4000386
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发表时间：
2021-02
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影响因子：
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通讯作者：
Ella Y. Wang;Anirudh Som;Ankita Shukla;Hongjun Choi;P. Turaga
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作者：
Ella Y. Wang;Anirudh Som;Ankita Shukla;Hongjun Choi;P. Turaga
由于深度神经网络能够提高多个诊断任务的性能，因此它们越来越多地用作医疗保健应用中的辅助工具。然而，由于基于深度学习的系统在可靠性、可推广性和可解释性方面的实际限制，这些方法在临床环境中没有被广泛采用。因此，已经开发了在网络训练期间施加额外约束的方法，以获得更多控制并提高可解释性，促进其在医疗保健界的接受。在这项工作中，我们研究了使用正交球（OS）约束对来自胸部X射线图像的COVID-19病例进行分类的益处。OS约束可以写成一个简单的正交项，在分类网络训练期间与标准交叉熵损失结合使用。以前的研究已经证明了将这些约束应用于深度学习模型的显著好处。我们的研究结果证实了这些观察结果，表明正交性损失函数有效地通过GradCAM可视化产生改进的语义定位，增强分类性能，并减少模型校准误差。我们的方法实现了1.6%和4.8%的两个和三个类分类的准确性，分别提高;类似的结果被发现的模型与数据增强应用。除了这些发现之外，我们的工作还提出了OS正则化器在医疗保健中的新应用，提高了COVID-19分类的深度学习模型的事后可解释性和性能，以促进这些方法在临床环境中的采用。我们还确定了我们的策略的局限性，可以探索在未来的进一步研究。
Deep neural networks have increasingly been used as an auxiliary tool in healthcare applications, due to their ability to improve performance of several diagnosis tasks. However, these methods are not widely adopted in clinical settings due to the practical limitations in the reliability, generalizability, and interpretability of deep learning based systems. As a result, methods have been developed that impose additional constraints during network training to gain more control as well as improve interpretabilty, facilitating their acceptance in healthcare community. In this work, we investigate the benefit of using Orthogonal Spheres (OS) constraint for classification of COVID-19 cases from chest X-ray images. The OS constraint can be written as a simple orthonormality term which is used in conjunction with the standard cross-entropy loss during classification network training. Previous studies have demonstrated significant benefits in applying such constraints to deep learning models. Our findings corroborate these observations, indicating that the orthonormality loss function effectively produces improved semantic localization via GradCAM visualizations, enhanced classification performance, and reduced model calibration error. Our approach achieves an improvement in accuracy of 1.6% and 4.8% for two- and three-class classification, respectively;similar results are found for models with data augmentation applied. In addition to these findings, our work also presents a new application of the OS regularizer in healthcare, increasing the post-hoc interpretability and performance of deep learning models for COVID-19 classification to facilitate adoption of these methods in clinical settings. We also identify the limitations of our strategy that can be explored for further research in future.