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...

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Detalles Bibliográficos
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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2022
Materias:
ORIGINAL REPORTS
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

Internet

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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