论文标题
通过对抗训练和双批归一化来提高神经网络的诊断性能和临床可用性
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
论文作者
论文摘要
揭示机器学习模型的决策过程对于在临床实践中实施诊断支持系统至关重要。在这里，我们证明了受对抗训练的模型可以显着提高病理检测的可用性，而其标准对应物则可以显着提高病理检测的可用性。我们让六名经验丰富的放射科医生对X射线，计算机断层扫描和磁共振成像扫描数据集中显着性图的可解释性进行了评估。发现了我们的对抗模型的显着改善，通过应用双批归一化可以进一步改善。与以前对受对抗训练模型的研究相反，我们发现当使用了足够大的数据集和双批批准训练时，此类模型的准确性等于标准模型。为了确保可传递性，我们还在22,433 X射线的外部测试集上验证了结果。这些发现阐明了在训练过程中需要不同的对抗和真实图像的路径，以实现具有卓越的临床可解释性的最先进结果。
Unmasking the decision-making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts. We let six experienced radiologists rate the interpretability of saliency maps in datasets of X-rays, computed tomography, and magnetic resonance imaging scans. Significant improvements were found for our adversarial models, which could be further improved by the application of dual batch normalization. Contrary to previous research on adversarially trained models, we found that the accuracy of such models was equal to standard models when sufficiently large datasets and dual batch norm training were used. To ensure transferability, we additionally validated our results on an external test set of 22,433 X-rays. These findings elucidate that different paths for adversarial and real images are needed during training to achieve state of the art results with superior clinical interpretability.
