Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers

Sourya Sengupta; Craig K. Abbey; Kaiyan Li; Mark A. Anastasio

doi:10.1117/12.2613220

4 April 2022 Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers

Sourya Sengupta, Craig K. Abbey, Kaiyan Li, Mark A. Anastasio

Proceedings Volume 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment; 1203514 (2022) https://doi.org/10.1117/12.2613220
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

The use of convolutional neural networks (CNNs) for establishing anthropomorphic numerical observers (ANOs) is being actively explored. In these data-driven approaches, CNNs are trained in a standard supervised way with human-labeled training data; hence, the anthropomorphic component of this procedure resides only in the training labels. However, it is well-known that such traditionally trained CNNs can rely on image features that are highly specific to the training distribution and may not align with features exploited by human perception. While being able to predict human observer performance under certain specified conditions, traditionally-trained CNNs lack the interpretability and robustness that may be desired for an ANO. To address this, in this work we investigate the use of an adversarial robust training strategy for training CNN-based observers. As recently demonstrated in the computer vision literature, this training strategy can result in CNNs that exploit more human-interpretable features than would be employed by a standard CNN. Robustly trained CNNs are systematically investigated for performing a signal-known-exactly (SKE) and background-known-statistically (BKS) binary detection task. Additionally, a differential evolution-based optimization procedure is developed to establish robustly trained CNNs that achieve a specified performance, which may provide a new approach to establishing ANOs.

Citation Download Citation

Sourya Sengupta, Craig K. Abbey, Kaiyan Li, and Mark A. Anastasio "Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers", Proc. SPIE 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 1203514 (4 April 2022); https://doi.org/10.1117/12.2613220

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image classification

Visual process modeling

Performance modeling

Data modeling

Visualization

Signal detection

Binary data

Show All Keywords

Keywords/Phrases

Search In:

Publication Years