Dataset Information

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 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.

SUBMITTER: Han T

PROVIDER: S-EPMC8280105 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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

Han Tianyu T Nebelung Sven S Pedersoli Federico F Zimmermann Markus M Schulze-Hagen Maximilian M Ho Michael M Haarburger Christoph C Kiessling Fabian F Kuhl Christiane C Schulz Volkmar V Truhn Daniel D

Nature communications 20210714 1

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 foun ...[more]

PMID: 34262044

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Dataset Information

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

Publications

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

Similar Datasets

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