I'm trying to solve a medical imaging regression problem using a CNN. Each of the samples in my data set consists of one, two, or three of the following file types:
Each of the files is a three- or four-dimensional matrix of voxel values between 0 and 255.
I don't want to throw information away since I have a limited number of data samples. Is there a technique for working around those cases which have a 'missing' file (or two)? For example, could I construct a matrix consisting of, say, all zeroes of the correct dimension and size and use this as a replacement for the file? Would this work or would it lead to problems with the CNN? Of course, the samples that I want to make inferences on might also have missing files, so, maybe, a matrix with all zeroes would be a type of information.
Anyway, what does one do in a case like this?