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, the authors demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts.