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Hi,
I was wondering how UNet deals with the sliding window input.
Because the ROI you set is bigger than the patches UNet is trained on.
How does this work?
Thanks.
Kirsten
The text was updated successfully, but these errors were encountered:
From what I understand, UNet is consists of CNNs.
So it can take input of different shape.
Like you trained it in [96, 96, 96], but you can inference it in [160,160,160] cubes.
The advantage of inference it using larger cubes is that you get faster inference speed.
Thank you. Do you have some sort of article? Because everything I am reading suggest to use same size. Also, when using a larger sliding window, my results can differ dramatically.
Hi,
I was wondering how UNet deals with the sliding window input.
Because the ROI you set is bigger than the patches UNet is trained on.
How does this work?
Thanks.
Kirsten
The text was updated successfully, but these errors were encountered: