Meeting Banner
Abstract #2402

Task Performance or Artifact Reduction? Evaluating the Number of Channels and Dropout based on Signal Detection on a U-Net with SSIM Loss

Rachel E Roca1, Joshua D Herman1, Alexandra G O'Neill1, Sajan G Lingala2, and Angel R Pineda1
1Mathematics Department, Manhattan College, Riverdale, NY, United States, 2Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States

The changes in image quality caused by varying the parameters and architecture of neural networks are difficult to predict. It is important to have an objective way to measure the image quality of these images. We propose using a task-based method based on detection of a signal by human and ideal observers. We found that choosing the number of channels and amount of dropout of a U-Net based on the simple task we considered might lead to images with artifacts which are not acceptable. Task-based optimization may not align with artifact minimization.

This abstract and the presentation materials are available to members only; a login is required.

Join Here