To clarify: they used data which was determined as unusable as it was too noisy for regular algorithms. So the neural net was trained on more data than just a couple of images. They were trained on all the raw data available. Even if the results might not be that accurate, it is actually a good way of solving this type of problem. Scientifically speaking, the results are not accurate, but it might give us a new perspective to the problem.