Thank you for your answer. Just one more question, if we use zero padding instead of interpolation to resize the images (from 112 to 128) dose it affect the performance of Unet in terms of Dice score for example ?
In my experiments I just resized the images using zero padding instead of interpolation and I trained the Unet. The dice with zero padding was 0.6807 while with interpolation it was 0.8304. I am wondering if this drop in performance is natural or I am doing something wrong. Couldn’t find anything in net.
By resizing with zero padding, I mean resizing the volumes of size (112x40x40) to (128x128x128). Just one point is that. the dataset that I am working with it is very small and imbalanced. I am trying to detect some signals from noise where in each volume we have less than 0.1 percent signal. Could it be a reason that model dose not work with zero padding?
Thank you.