On the role of deep learning model complexity in adversarial robustness for medical images

被引：0
作者：
David Rodriguez
Tapsya Nayak
Yidong Chen
Ram Krishnan
Yufei Huang
机构：
[1] University of Texas at San Antonio,Department of Electrical and Computer Engineering
[2] University of Texas Health San Antonio,Greehey Children’s Cancer Research Institute
[3] University of Texas Health San Antonio,Department of Population Health Sciences
[4] University of Pittsburgh,Department of Medicine, School of Medicine, UPMC Hillman Cancer Center
来源：
BMC Medical Informatics and Decision Making | / 22卷
关键词：
Adversarial attacks; Perturbation; Adversarial robustness; Medical image classification; Model complexity;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 69 条
[1]  
Russakovsky O(2015)Imagenet large scale visual recognition challenge Int J Comput Vis 115 211-252
[2]  
Deng J(2020)The state of artificial intelligence-based fda-approved medical devices and algorithms: an online database NPJ Digital Med. 3 1-8
[3]  
Su H(2017)Dermatologist-level classification of skin cancer with deep neural networks Nature 542 115-118
[4]  
Krause J(2016)Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs JAMA 316 2402-2410
[5]  
Satheesh S(2021)Universal adversarial attacks on deep neural networks for medical image classification BMC Med Imaging 21 1-13
[6]  
Ma S(2020)Understanding adversarial attacks on deep learning based medical image analysis systems Pattern Recognit 9 2579-2605
[7]  
Huang Z(2008)Visualizing data using t-sne J Mach Learn Res 12 2825-2830
[8]  
Karpathy A(2011)Scikit-learn: machine learning in python J Mach Learn Res 3 638-1131
[9]  
Khosla A(2018)Mlxtend: Providing machine learning and data science utilities and extensions to python’s scientific computing stack J. Open Source Softw. 172 1122-undefined
[10]  
Bernstein M(2018)Identifying medical diagnoses and treatable diseases by image-based deep learning Cell undefined undefined-undefined
1234567