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On the role of deep learning model complexity in adversarial robustness for medical images
BACKGROUND: Deep learning (DL) models are highly vulnerable to adversarial attacks for medical image classification. An adversary could modify the input data in imperceptible ways such that a model could be tricked to predict, say, an image that actually exhibits malignant tumor to a prediction that...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208111/ https://www.ncbi.nlm.nih.gov/pubmed/35725429 http://dx.doi.org/10.1186/s12911-022-01891-w |