Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization

Tianyu Han (), Sven Nebelung, Federico Pedersoli, Markus Zimmermann, Maximilian Schulze-Hagen, Michael Ho, Christoph Haarburger, Fabian Kiessling, Christiane Kuhl, Volkmar Schulz () and Daniel Truhn ()
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Tianyu Han: RWTH Aachen University
Sven Nebelung: University Hospital Aachen
Federico Pedersoli: University Hospital Aachen
Markus Zimmermann: University Hospital Aachen
Maximilian Schulze-Hagen: University Hospital Aachen
Michael Ho: ARISTRA GmbH
Christoph Haarburger: ARISTRA GmbH
Fabian Kiessling: RWTH Aachen University
Christiane Kuhl: University Hospital Aachen
Volkmar Schulz: RWTH Aachen University
Daniel Truhn: University Hospital Aachen

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: 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 are found for our adversarial models, which are further improved by the application of dual-batch normalization. Contrary to previous research on adversarially trained models, we find that accuracy of such models is equal to standard models, when sufficiently large datasets and dual batch norm training are used. To ensure transferability, we additionally validate 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.

Date: 2021
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DOI: 10.1038/s41467-021-24464-3

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