A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection

Linhai Ma; Jiasong Chen; Linchen Qian; Liang Liang

doi:10.1117/12.3006534

2 April 2024 A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection

Linhai Ma, Jiasong Chen, Linchen Qian, Liang Liang

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292613 (2024) https://doi.org/10.1117/12.3006534
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

It is known that deep neural networks (DNNs) are vulnerable to adversarial noises. Improving adversarial robustness of DNNs is essential. This is not only because unperceivable adversarial noise is a threat to the performance of DNNs models, but also adversarially robust DNNs have a strong resistance to the white noises that may present everywhere in the actual world. To improve adversarial robustness of DNNs, a variety of adversarial training methods have been proposed. Most of the previous methods are designed under one single application scenario: image classification. However, image segmentation, landmark detection, and object detection are more commonly observed than classifying the entire images in the medical imaging field. Although classification tasks and other tasks (e.g., regression) share some similarities, they also differ in certain ways, e.g., some adversarial training methods use misclassification criteria, which is well-defined in classification but not in regression. These restrictions/limitations hinder application of adversarial training for many medical imaging analysis tasks. In our work, the contributions are as follows: (1) We investigated the existing adversarial training methods and discovered the challenges that make those methods unsuitable for adaptation in segmentation and detection tasks. (2) We modified and adapted some existing adversarial training methods for medical image segmentation and detection tasks. (3) We proposed a general adversarial training method for medical image segmentation and detection. (4) We implemented our method in diverse medical imaging tasks using publicly available datasets, including MRI segmentation, Cephalometric landmark detection, and blood cell detection. The experiments substantiated the effectiveness of our method.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Linhai Ma, Jiasong Chen, Linchen Qian, and Liang Liang "A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292613 (2 April 2024); https://doi.org/10.1117/12.3006534

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