[cornholio]

> We conducted imaging on healthy volunteers, capturing brain, spine, abdomen, lung, musculoskeletal, and cardiac images. Deep learning signal prediction effectively eliminated EMI signals, enabling clear imaging without shielding.

So essentially, the neural net was trained to what a healthy MRI looks like and would, when exposed to abnormal structures, correct them away as EMI noise leading to wrong diagnostics?

I won't be very dismissive of this approach and probably deep learning has a strong role to play in improving medical imaging. But this paper is far, far from sufficient to prove it. At a minimum, it would require mixed healthy / abnormal patients with particularities that don't exist in the training set, and each diagnostic reconfirmed later on a high resolution machine. You need to actually prove the algorithm does not distort the data, because an MRI that hallucinates a healthy patient is much more dangerous than no MRI at all.

[rossant]

Seems like a huge and obvious red flag to me indeed. I can't imagine how the authors managed to not even mention the issue in the abstract. If the model is trained on healthy scans, well, yes, it will spit out healthy scans. The whole point of clinical radiology is to get enough precision to detect (potentially subtle) anomalies.

[nullc]

I don't think that (necessarily) says what you think it says.

You can read that as saying that the DL eliminated the background noise rather than saying that the system was conditioned on images of healthy people. From that it may well have been conditioned on just an empty machine or neutral test samples.

If so, there may be a good reason to suspect that it isn't likely to create artifacts that look like or mask anatomical structures.

[cornholio]

You can read it like that, but they surely didn't prove it works like that and the burden of proof is squarely on them.

Realistically, the training set is most likely MRIs of similar tissues and would be naturally biased towards healthy structures. Even the remotest possibility of a hallucination should be addressed and disproved for such an application but they make no mention of it, just "OMG magic ENHANCE button!".

[hackerlight]

If the noise exists only on a certain frequency then the model would learn a passband filter of sorts and won't necessarily filter out abnormal structures. But they'd need to verify that.