Investigating Usable Information for Assessing the Impact of Medical Image Processing

The data processing inequality (DPI) in information theory posits that no data processing can increase the mutual information between data and their task labels. For any post-processing method, the mutual information between post-processed images and task labels should always be less than or, at best, equal to the mutual information between raw images and diagnostic task labels. This is consistent with the fact that the performance of an ideal Bayesian observer cannot be improved through image processing. As such, mutual information is generally not suitable for evaluating the effects of image processing. Recently, a novel variant of mutual information, termed V-information (V-info), has been introduced to account for the computational constraints associated with a sub-ideal observer. In contrast to conventional mutual information, V-info can increase as a result of processing of data, making it a promising task-oriented metric for assessing the impact of image processing. In this study, for the first time, we investigate the application of V-info, which we refer to as the more readily meaningful term”observable-usable information” (O-U-Info), for evaluating the impact of medical image processing. Specifically, we examine deep learning-based super-resolution as the image processing operation. A deep learning-based numerical observer (NO) is employed to perform a Rayleigh binary signal discrimination task using low-resolution, high-resolution, and super-resolved images. We quantify O-U-Info under conditions of varying NO capacity and dataset size. The results demonstrate the potential usefulness of O-U-Info as an objective metric for assessing the impact of medical image processing.