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.
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