Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
Abstract
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.
Keywords
Cite
@article{arxiv.2011.13011,
title = {Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization},
author = {Tianyu Han and Sven Nebelung and Federico Pedersoli and Markus Zimmermann and Maximilian Schulze-Hagen and Michael Ho and Christoph Haarburger and Fabian Kiessling and Christiane Kuhl and Volkmar Schulz and Daniel Truhn},
journal= {arXiv preprint arXiv:2011.13011},
year = {2021}
}