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Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology
Deep learning (DL) models have rapidly become a popular and cost-effective tool for image classification within oncology. A major limitation of DL models is their vulnerability to adversarial images, manipulated input images designed to cause misclassifications by DL models. The purpose of the study...
Autores principales: | Joel, Marina Z., Umrao, Sachin, Chang, Enoch, Choi, Rachel, Yang, Daniel X., Duncan, James S., Omuro, Antonio, Herbst, Roy, Krumholz, Harlan M., Aneja, Sanjay |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8932490/ https://www.ncbi.nlm.nih.gov/pubmed/35271304 http://dx.doi.org/10.1200/CCI.21.00170 |
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