Adversarial robustness study of convolutional neural network for lumbar disk shape reconstruction from MR images

Jiasong Chen; Linchen Qian; Timur Urakov; Weiyong Gu; Liang Liang

doi:10.1117/12.2580852

15 February 2021 Adversarial robustness study of convolutional neural network for lumbar disk shape reconstruction from MR images

Jiasong Chen, Linchen Qian, Timur Urakov, Weiyong Gu, Liang Liang

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 1159615 (2021) https://doi.org/10.1117/12.2580852
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Machine learning technologies using deep neural networks (DNNs), especially convolutional neural networks (CNNs), have made automated, accurate, and fast medical image analysis a reality for many applications, and some DNN-based medical image analysis systems have even been FDA-cleared. Despite the progress, challenges remain to build DNNs as reliable as human expert doctors. It is known that DNN classifiers may not be robust to noises: by adding a small amount of noise to an input image, a DNN classifier may make a wrong classification of the noisy image (i.e., in-distribution adversarial sample), whereas it makes the right classification of the clean image. Another issue is caused by out-of-distribution samples that are not similar to any sample in the training set. Given such a sample as input, the output of a DNN will become meaningless. In this study, we investigated the in-distribution (IND) and out-of-distribution (OOD) adversarial robustness of a representative CNN for lumbar disk shape reconstruction from spine MR images. To study the relationship between dataset size and robustness to IND adversarial attacks, we used a data augmentation method to create training sets with different levels of shape variations. We utilized the PGD-based algorithm for IND adversarial attacks and extended it for OOD adversarial attacks to generate OOD adversarial samples for model testing. The results show that IND adversarial training can improve the CNN robustness to IND adversarial attacks, and larger training datasets may lead to higher IND robustness. However, it is still a challenge to defend against OOD adversarial attacks.

Conference Presentation

Citation Download Citation

Jiasong Chen, Linchen Qian, Timur Urakov, Weiyong Gu, and Liang Liang "Adversarial robustness study of convolutional neural network for lumbar disk shape reconstruction from MR images", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 1159615 (15 February 2021); https://doi.org/10.1117/12.2580852

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available