> This makes it hard to predict when and how deep learning methods will fail (there are no theoretical guarantees that deep learning will work).
I actually think we know fairly well how deep learning methods work (and what the shortcomings are), we just have no way to interpret the models it produces. Wouldn't ML techniques to reduce scan times fail at the most critical moments, ie when patients had unusual or unexpected ailments? Using ML in on downsampled MRI images feels akin to having an artist with a lot of familiarity of human anatomy touch up a scan.
I speculate (and hope) that ML diagnostics will help give medical care access to really poor people in really poor countries. There's not enough cheap doctors to help all of them, and if ML can speed things up and reduce marginal costs to zero, even if it degrades quality of care, a lot of lives could be saved. 80% ML + 20% human is better than no medical care at all.
I'd argue that knowing fairly well and theoretical guarantees are significantly different.
As an example, you can run a million simulations on a satellite with different initial conditions to test your new control algorithm. However, you have infinitely many possible initial conditions, and you can't simulate all of them. If you however show that the closed loop system in stable sine sense, it's a more rigorous guarantee.
Whether that is acceptable or not depends on the purpose of the scan. If it's for a routine and defined purpose, like measuring the size of something, a bit of artistic licence by the computer is not too bad, I think. As long as it gets the size right (or whatever specific aspect is relevant)
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, anything it creates is simply based on the statistical model it formed from the training set.
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
> Though compressed sensing can improve the image quality relative to a vanilla inverse Fourier transform, it still suffers from artifacts.
Odd remark. FDA approves compressed sensing products (e.g., [1], [2], [3], [4]) precisely because it is possible (and provably so) to quantify and/or characterize such “artifacts” up to substantial equivalence.
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 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 resolution scan was regularized with a neural network? The doctor might miss small tumors due to low resolution too. I understand your concern, but I wouldn't dismiss it so outright.
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.
You'll be surprised how many well-known doctors miss a ton of non-obvious anomalies in scan results. There's no way a doctor would have seen all prior records of confirmed diagnosis and their corresponding scans.
In an ideal world, a deep learning algorithm should provide an independent report of potential features of interest to a doctor to let him know if something that he could have missed. However, I hope it stays in the intended role and doesn't make the doctor less careful.
Could this be taken one step further... Use the ML in-the-loop during an MRI scan, to look at the data collected so far, then decide which frequency should be measured next to most improve the quality of the result?
This can also all be simulated offline without an MRI machine to test on with just access to a few full scans... So could be a good weekend project for someone here on HN, and your technique might even be in use by the time you need an MRI scan and will mean your doctor can get results slightly quicker and you get better healthcare, together with hundreds of millions of other people!
tqi|4 years ago
I actually think we know fairly well how deep learning methods work (and what the shortcomings are), we just have no way to interpret the models it produces. Wouldn't ML techniques to reduce scan times fail at the most critical moments, ie when patients had unusual or unexpected ailments? Using ML in on downsampled MRI images feels akin to having an artist with a lot of familiarity of human anatomy touch up a scan.
csee|4 years ago
tangoed|4 years ago
As an example, you can run a million simulations on a satellite with different initial conditions to test your new control algorithm. However, you have infinitely many possible initial conditions, and you can't simulate all of them. If you however show that the closed loop system in stable sine sense, it's a more rigorous guarantee.
HPsquared|4 years ago
olliej|4 years ago
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, anything it creates is simply based on the statistical model it formed from the training set.
davidhyde|4 years ago
unknown|4 years ago
[deleted]
vitorsr|4 years ago
Odd remark. FDA approves compressed sensing products (e.g., [1], [2], [3], [4]) precisely because it is possible (and provably so) to quantify and/or characterize such “artifacts” up to substantial equivalence.
[1] https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn...
[2] https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn...
[3] https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn...
[4] https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn...
bick_nyers|4 years ago
01acheru|4 years ago
I don’t want to be part of it, thanks
notarandomer|4 years ago
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.
tangoed|4 years ago
In an ideal world, a deep learning algorithm should provide an independent report of potential features of interest to a doctor to let him know if something that he could have missed. However, I hope it stays in the intended role and doesn't make the doctor less careful.
ryan93|4 years ago
londons_explore|4 years ago
This can also all be simulated offline without an MRI machine to test on with just access to a few full scans... So could be a good weekend project for someone here on HN, and your technique might even be in use by the time you need an MRI scan and will mean your doctor can get results slightly quicker and you get better healthcare, together with hundreds of millions of other people!
nik282000|4 years ago