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Comment by lostlogin | Hacker News Reader

AI makes up for its poor reporting by enhancing the images.

Current Siemens MR software ‘Deep Resolve’ makes up the signal (adding about 50%), then makes up every second pixel, and then, for 3D sequences, makes up every second slice. It’s locking about 59% of the time off each sequences. And it’s really really good. I’m an MR tech.

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but those are two different things. Of course something like Deep Resolve is great, as are modern model based reconstruction algorithms for CTs, but here we are talking about LLMs and their ability to interpret medical images, which has nothing to do with what you said.

Sorry? You use AI to hallucinate medical images and that's good?

It is not really the same as LLMs. I wouldn't call it AI. And I wouldn't say "makes up". I work in this field and this is certainly based also in part on my research.

‘Makes up’ is inaccurate for sure. But it’s not strictly true to call it acquired data either.

After years of collecting artifacts and errors, I have more and more respect for the tool.

But it’s jarring. I open a sequence, decrease the acquired resolution, add the AI and get a scan that’s quicker and higher resolution.

It’s an amazing time to be an MR tech.

It is amazing. It is the result of two decades of research in image reconstruction algorithms. The machine learning is part of it, but that it is sold as "AI" has probably more to do with marketing.

It certainly has a lot of marketing behind it.

https://marketing.webassets.siemens-healthineers.com/2861d15...

I haven't seen it marketed as "AI" by GE, Siemens or Philips. They usually gesture at "deep learning" or "compressed sensing".

No radiologist is buying "AI" scanners. Radiologists are probably among the most jaded of an audience about the word "AI" due to decades of undelivered promises. AI is synonymous with "worthless trash" to them, not to mention everyone says "AI" is going to put them out of work. lol

Super-resolution is certainly distinct from hallucinating - it just rearranged data that was already there to make it easier for the human eye to see - but should be used with care. I can easily imagine that an upscaling algorithm makes it so a certain defect is clearly not present, when the source image is ambiguous (which the radiologist would have noticed), and in reality the defect is present.

I would definitely be wary using the more advanced super resolution schemes. It took some work preventing it from drawing faces everywhere.

MRI is already a form of compressed sensing, I would much prefer statistical forms of super resolution to ones based on training data. Even if it is only trained on MRIs it will see some noise and plausibly expand it into whatever disease fits.

It's just DLSS/Frame Generation for MRI's.

Most upscaling and super-resolution techniques I’ve seen use various implementations of interpolation; typically nearest-neighbor approaches. Although I don’t work in the medical field and haven’t checked in on the research at least since ViTs overtook CNNs for other areas of computer vision.

Sure but claude and ChatGPT are not Siemens 'Deep resolve'.

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