@davecg, I did something similar in leaderboard 2 but couldn't get better AUROC than max prediction on single view. Overfitting was a problem because the training was converging very fast on traing set (demonstrating the usefulness of the solution in my opinion).
But the exact spatial correlation is probably better than rough estimation (distance to the nipple). What I can understand of deep CNN theory is it can extract the features potentially and keep (or not depending on the weights) the relation between the features. So a properly trained network could potentially (by theory) learn by itself the spatial correlation between multiple features (cancer position vs gland position, cancer pos vs nipple pos, cancer pos vs skin pos, etc. ) in 2 (or n) different channels.
High resolution morphology (contours, form, density), Low resolution 3D spatial correlation between views, High resolution temporal (with past exam) correlation (growth vs no growth) are key parameters of this problem to max AUROC (at least human AUROC) in my opinion.
My personal advice from a 10 year experience general radiologist to a current resident in radiology ... train in some kind of interventional radiology. Interventions will be harder to automate than image diagnosis in short/mid term. Robotic should logically lag computer vision in radiology in a 10-20 years future. And of course keep interest in machine learning as you do !