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

Medicine and Health

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

T. Han, S. Nebelung, et al.

This innovative research conducted by Tianyu Han and colleagues reveals how adversarially trained neural networks can enhance pathology detection and clinical usability. By employing dual batch normalization, the study showcases improved interpretability of saliency maps, validated across diverse datasets, underscoring the demand for tailored training techniques in real-world imaging.

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