Supervised learning with tagged images
I have worked on similar projects (using medical images such as PET to predict outcomes).  A method being used more and more for predicting cancer treatment outcomes is texture analysis: https://www.ncbi.nlm.nih.gov/pubmed/21321270

Another method of texture analysis uses wavelet transforms: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3505569/

After deriving the texture features, you could then use those in a ML model for making predictions.

A benefit to texture analysis is that it is mostly independent of the size of the target.  It is however, more dependent on resolution. If your images are produced on different scanners, you may need to resample in order to normalize the images.

There are fewer studies about deep learning for medical imaging, but it definitely has some exciting potential.  The question is whether or not a CNN can pick up subtle differences.  From what I've seen (I'll look for a reference), CNN hasn't outperformed any other method yet.

Maybe add a few more details so we can get a better idea of what your objective is?