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As we are building our internal MRI database, we progressively list the images that have excessive artifacts in the file exclude.yaml. We should exploit that information and train a model that automatically detects if an images has excessive artifact. When processing large cohorts, that information would be very useful to have.
Todo: make sure that our exclude.yaml uses the same vocabulary to identify artifacts.
The text was updated successfully, but these errors were encountered:
That's a very nice and useful idea. With the numerous problematic images in CanProCo, I think it can easily be trained (at least on the PSIR contrast : examples here).
As we are building our internal MRI database, we progressively list the images that have excessive artifacts in the file
exclude.yaml
. We should exploit that information and train a model that automatically detects if an images has excessive artifact. When processing large cohorts, that information would be very useful to have.Todo: make sure that our
exclude.yaml
uses the same vocabulary to identify artifacts.The text was updated successfully, but these errors were encountered: