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

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 counterp...

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Detalles Bibliográficos
Autores principales: Han, Tianyu, Nebelung, Sven, Pedersoli, Federico, Zimmermann, Markus, Schulze-Hagen, Maximilian, Ho, Michael, Haarburger, Christoph, Kiessling, Fabian, Kuhl, Christiane, Schulz, Volkmar, Truhn, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280105/
https://www.ncbi.nlm.nih.gov/pubmed/34262044
http://dx.doi.org/10.1038/s41467-021-24464-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280105/
https://www.ncbi.nlm.nih.gov/pubmed/34262044
http://dx.doi.org/10.1038/s41467-021-24464-3

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