BMC Med Informatics & Decision Making: On the Role of Deep Learning Model Complexity in Adversarial Robustness for Medical Images (Chen Lab)


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 a malignant tumor to a prediction that it is benign. However, the adversarial robustness of DL models for medical images is not adequately studied. DL in medicine is inundated with models of various complexity—particularly, very large models. In this work, we investigate the role of model complexity in an adversarial setting.

Read Full Text

Article Categories: Research Paper

Since 2004, UT Health San Antonio, Greehey Children’s Cancer Research Institute’s (Greehey CCRI) mission has been to advance scientific knowledge relevant to childhood cancer, contribute to understanding its causes, and accelerate the translation of knowledge into novel therapies. Greehey CCRI strives to have a national and global impact on childhood cancer by discovering, developing, and disseminating new scientific knowledge. Our mission consists of three key areas — research, clinical, and education.

Stay connected with the Greehey CCRI on Facebook, Twitter, LinkedIn, and Instagram.