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16 - 20 February 2025
San Diego, California, US
Conference 13409 > Paper 13409-27
Paper 13409-27

Investigating usable information for assessing the impact of medical image processing

18 February 2025 • 5:10 PM - 5:30 PM PST | Palm 7

Abstract

The data processing inequality from information theory states that the information content of an image cannot be increased by image processing. This is consistent with the fact that the performance ideal Bayesian observer cannot be improved by image processing. However, it is well known that processing of images can, in certain cases, improve the performance of sub-ideal observers, including humans. For this reason, traditional information theory metrics have limited value for analyzing the impact of image processing. Recently, a new measure of information, variational information (V-info), has been proposed that accounts for the characteristics of a sub-ideal observer. Unlike traditional mutual information, V-info can be increased by image processing. As such, it can serve to predict when image processing can improve the performance of a specified sub-ideal observer on a specified task. In this study, for the first time, we investigate V-info for assessing the impact of medical image processing. The results demonstrate the potential utility of V-information as a metric for objectively assessing the impact of medical image processing.

Presenter

Changjie Lu
Univ. of Illinois (United States)
Changjie Lu is a second-year Ph.D. student in the Department of Bioengineering at the University of Illinois at Urbana-Champaign. His main research focus is on the application of deep learning in medical imaging and the theory of imaging systems. He earned his Bachelor's degree in Mathematics and Applied Mathematics in 2023.
Application tracks: AI/ML
Presenter/Author
Changjie Lu
Univ. of Illinois (United States)
Author
Univ. of Illinois (United States)
Author
Hua Li
Washington Univ. in St. Louis (United States)
Author
Univ. of Illinois (United States)