Hi all,
I am trying to replicate experiments done with autoencoder in the following article : https://arxiv.org/pdf/1606.08921.pdf 31
Basically, I want to use an autoencoder to “filter” noise and artifacts from image, and more specifically in my case, medical MRI images of the brain.
I’ve tried some experiments with MNIST datasets, but obviously that is not the end goal. I’d like to build my custom dataset. I already have built an image library (in .png format). The inputs would be the noisy images with artifacts, while the outputs would be the clean images. As of now, I have my images in two folders structured like this :
Folder 1 - Clean images
img1.png
img2.png
…
imgX.png
Folder 2 - Transformed images
img1_transform1.png
img1_transform2.png
…
img1_transformY.png
…
imgX_transformY.png
So basically, I have, for each of the X “clean” images, Y different transformations (various level of noises, artifacts, etc.).
Does anyone have any idea on how to build a custom dataset for that kind of experiment (or a link to a detailed tutorial)? I’ve read on other topics but since I’m also quite new to PyTorch, I don’t really understand everything and all I’ve tried so far has failed miserably.
Thank you!