3D convolution, de-convolution layers in Flux for training 3D MRI data

Are there 3D convolution, de-convolution layers in Flux.jl?
Or is there any implementation that can be used with Flux.jl?

To elaborate I have 3D greyscale images (MRI scans), i.e. each voxel value is a number. Now is there convolutional layer that can accept this kind of input?

** EDIT: I misread this issue on the GitHub repo. As Azamat explains below, Flux.jl does support 3D convolution. **

Flux does support convolution layers for 3D MRI. It does not support dimensions higher than 3 yet.


Hi @Azamat. I was looking into doing similar work, i.e. convolution to train 3D MRI for image reconstruction. Would you be kind enough to provide some examples, if you’ve worked on this? Or a direction? :slight_smile: ta

It’s a WIP, but have a look at GitHub - Dale-Black/MedicalTutorials.jl

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