woah. woah. woah. hold on a second here... are we comfortable enough with understanding all of the behavior of deep learning models to where we can confidently put them in the pipeline for diagnostic clinical imaging? i'm okay with using them for image analysis, but denoising and other image production tasks seems dangerous. how do you know what you're looking at is real as opposed to something that just looks convin…
I have seen an alarming number of talks where someone proposes to algorithmically add Gado contrast or turn a T1 into a T2 image. In a few very specific contexts, this makes sense (e.g., aligning a T1 taken in one session with a T2 taken in another). Otherwise though, it seems dangerous to mistake a "real" image with the expected image given another one.