I have a dataset of 3D images (volumes) with dimensions 400x250x400. For each input image I have an output of the same dimensions. I would like to train a machine learning (or deep learning) model on this data in order to predict values with new data.
My main problems are :
Images are very big, which leads to memory issues (tried with an NVIDIA 2080Ti and doesn't fit on memory during training)
I need a very fast inference, because the model will be used on real time(speed is a requirment)
I already have experience with architectures such as 3D Unet using Keras with tensorflow backend, but it didn't worked for me because of the previous reasons, even with very few layers and convolution filters.
I know that one of the first solutions that one could imagine, is to reduce resolution of the volumes, but in my case I'm not allowed this because I would lose a lot of spatial information.
Any ideas or suggestions ? Maybe Neural Nets are not the best solution ? If not, what could I use ?
Thank you very much for your suggestions