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Interesting. Would love to see an example of a tumor so small a radiologist could see it but that a ML algorithm would smooth out
It is not about big or small, I don’t think you understand how ML work. And by the way, tumors can be really small.
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Interesting. Would love to see an example of a tumor so small a radiologist could see it but that a ML algorithm would smooth out
It is not about big or small, I don’t think you understand how ML work. And by the way, tumors can be really small.
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There is no unadulterated result, you are doing less sampling and relying on ML to fill the gaps. So you either have the ML reconstructed result or a subsampled MRI. Healthcare is an area where we need good and clean data as much as possible, let’s use ML reconstruction somewhere else.
Interesting. Would love to see an example of a tumor so small a radiologist could see it but that a ML algorithm would smooth out
That said, there are other great ways to incorporate DL into MRI other than recon. I'm more interested in the use of DL for image segmentation, feature detection, potentially denoising, or other techniques on the image processing side. Those make a lot more sense as "top down" tasks that are well suited for neural networks.
Can’t wait to do an MRI and hear the doc say “You’re all set, good to go!”, only to discover that I actually had a tumor but that really clever ML algorithm thought that it was noise and should’ve been smoothed out… I don’t want to be part of it, thanks
Note, I am biased because I research MRI acquisition and reconstruction methods and I am rolling out trials of fast MRI methods (that use some ML in the reconstruction) to find out how robust the methods actually are in practice.
Things like MRIs are the last thing you want to be using ML to invent detail in. This proposal basically says using ML we can quarter the number of frequencies we sample and still get good looking scans. But the full resolution is made by inventing details based on statistics from a biased input (most MRIs are taken due to something being wrong). Again, as with super resolution, ML cannot add detail that isn’t there,…
Playing devil's advocate here but can't machine learning be used to remove noise rather than add detail? Removing noise would reveal detail hidden in the data kind of like the result you get after applying a spectral filter to a fourier transformed image. For example: https://www.youtube.com/watch?v=s2K1JfNR7Sc
[*] For example, by measuring the quality of reconstruction of a known image (e.g. a real or digital phantom) or, in the ideal world, by evaluating clinical outcomes.
Can’t wait to do an MRI and hear the doc say “You’re all set, good to go!”, only to discover that I actually had a tumor but that really clever ML algorithm thought that it was noise and should’ve been smoothed out… I don’t want to be part of it, thanks
I am surprised you are so certain in this statement. There is always a tradeoff between scan time and image quality. Clinical scans often have thick slices to keep scan times reasonable. Using advanced reconstruction methods, e.g. ML, you can get thinner slices in the same scan time. How would you balance the benefit of getting higher resolution in the same time as a standard lower resolution scan time if the higher…
It looks like you are proposing some kind of mixed approach, not a simple “less data, faster scan, ML to the rescue”. I understand how MRI works but you are surely and obviously much more knowledgeable than me, so I simply wish you luck!
My problem with the article comes from reading that someone telling about using generative models on health data, I don’t think is time for this yet.
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Playing devil's advocate here but can't machine learning be used to remove noise rather than add detail? Removing noise would reveal detail hidden in the data kind of like the result you get after applying a spectral filter to a fourier transformed image. For example: https://www.youtube.com/watch?v=s2K1JfNR7Sc
The problem would be blurring or denoising meaningful information, but I don't know enough to say they don't do any denoising. I can imagine the data being noisy, but perhaps it isn't? shrug :D
Things like MRIs are the last thing you want to be using ML to invent detail in. This proposal basically says using ML we can quarter the number of frequencies we sample and still get good looking scans. But the full resolution is made by inventing details based on statistics from a biased input (most MRIs are taken due to something being wrong). Again, as with super resolution, ML cannot add detail that isn’t there,…
Earlier quoted context omitted.
I am surprised you are so certain in this statement. There is always a tradeoff between scan time and image quality. Clinical scans often have thick slices to keep scan times reasonable. Using advanced reconstruction methods, e.g. ML, you can get thinner slices in the same scan time. How would you balance the benefit of getting higher resolution in the same time as a standard lower resolution scan time if the higher…
Well, I really hope you succeed on your experiments, but right now it seems to me that ML introduces an error factor we still cannot precisely account for. It looks like you are proposing some kind of mixed approach, not a simple “less data, faster scan, ML to the rescue”. I understand how MRI works but you are surely and obviously much more knowledgeable than me, so I simply wish you luck! My problem with the articl…
Earlier quoted context omitted.
It is not about big or small, I don’t think you understand how ML work. And by the way, tumors can be really small.
Out of interest, do you understand how MRI reconstruction works?
If your question is more nuanced to mean “do you really really know how it works, meaning you could work on it tomorrow?” the answer is no, it is not my field.