The Imagenet-based networks already have bounding boxes to identify the region of interest. It's important, because many of the training images have multiple "things" in them, but in the '12 dataset, only one label. It's not as much "feature engineering" as "object discrimination in the training set".
That's not feature engineering, that's just a label for the task of interest (object localization).
Blanking out areas that are "not of interest" I would consider substantial feature engineering (unless the task you were training a net for was explicitly to find interesting vs. uninteresting areas).
There is a lot of stuff the human eye cant see that is very interesting. One of the challenges in medical imaging is getting accurate labelling of images. For many labellings we see large inter- and intra-observer variability. We have both the problem that humans see something that is not interesting and miss something that is interesting.
I currently work on estimating emphysema extent in CT lung scans. Emphysema can be very diffuse and it is not possible to label individual pixels, so instead we try to learn the local emphysema pattern from a global label. Neural networks are interesting for this problem because the learn the features, but it is also a "problem" because the features might not make physically sense, which could make it hard to transfer the model and convince clinicians that they should use it.
For that kind of task, you might want to filter out other things.
We should just be realistic.
We want to take real image, except it might be tinkered with, and make neural net tell us what we see on it, except we also want it to see what we can't see, and we want it to answer as accurate as possible, except we also want short and definitive answer.
We also kind of want it to admit that image always contains more than one thing, but kind of don't.
Pre-processing seems to be an answer, normalize the image, blank out areas obviously uninteresting to human eye, add dithering.