> I think any application of deep convolutional neural networks should be alongside a radiologist. If we speed up scans and make up for it with convnets it is very hard (practically speaking: impossible) to properly validate that they will not hallucinate away rare abnormalities. It will also be impossible for radiologists your spot errors like this in the wild because of the reduction in quality of the scan.
The benchmark for this technology is not perfection. The benchmark is human radiologists. Yes, this technology will miss things, so do humans. But if it's performing better than the humans we should prefer it, even if it's not perfect.
I think you have misunderstood the application, possibly because of the way the parent framed it.
This is not a project to interpret MRI data, it is a project to apply ML to accelerated scanning, i.e. inferring data that is not actually measured.
So it's a real problem, if a systematic bias attenuates some signals that would be interesting, there will be nothing there for a radiologist (or other ML system) to perform on.
Think of this as more of a "algorithmic super-resolution" approach.
I would probably use the term "data-driven machine hallucination" - which is pretty awesome. Though, I can see why radiologists would be wary of such an approach.
I will not say i understand it, but when an MRI aquires an image it is a rich k-space dataset. This data is then reduced to a single number (ie intensity) for each voxel in a MR image. Usually the k-space data is discarded after it is used to predict the voxel's intensity.
The benchmark for this technology is not perfection. The benchmark is human radiologists. Yes, this technology will miss things, so do humans. But if it's performing better than the humans we should prefer it, even if it's not perfect.