4 ms·

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 tho

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

ryan93 5y ago
Why would the ML algorithm necessarily change the scan. The radiologist could still look at the unadulterated MRI.
- lostlogin 5y ago
  MRI is completely adulterated at every stage. Algorithms and filters make the final result palatable. The raw data is a k-space data file. It’s not really human readable (though you can spot noise spikes etc).
- 01acheru 5y ago
  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.
  - ryan93 5y ago
    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
    - 01acheru 5y ago
      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.
      - exdsq 5y ago
        Out of interest, do you understand how MRI reconstruction works?
        01acheru 5y ago
        If your question is literally “you understand how it works?” the answer is yes, I do. 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.
      - ryan93 5y ago
        Not obvious the human chosen function for reconstruction is necessarily better than the ML one. The human function doesn't save all the data either.
    - acerjio 5y ago
      A lot of the problem comes from the use of generative neural networks. If the prior is that the reconstructed images should "look" a certain way, then the algorithm will favor that. Some of our colleagues did early work with DL and got scared off of generative models due to finding issues with nonphysical results (read: broken layers of cortex in the brain, completely non-physical anatomy) that these models can generate from the undersampled raw data. 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.
  - selimthegrim 5y ago
    There are definitely ways to work well with subsampled data, see Lester Mackey's recent work.
    - 01acheru 5y ago
      Could please share a link to the work you are referring to? I would really appreciate it (not ironically, it would be truly appreciated). I know we can work around subsampling, actually we have always been very good at it since our sampling data back in the day was way smaller than what we refer as subsampled today.
      - selimthegrim 5y ago
        http://stanford.edu/~lmackey/papers/thinning-slides.pdf http://stanford.edu/~lmackey/papers/thinning-slides.pdf
        01acheru 5y ago
        Thanks!
notarandomer 5y ago
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.
- 01acheru 5y ago
  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 article comes from reading that someone telling about using generative models on health data, I don’t think is time for this yet.
  - notarandomer 5y ago
    Thanks! I agree taht generative models on their own are definitely more risky than methods that combine ML for regularization along with data consistency terms that force the reconstructions to be consistent with the acquired data.
    - 01acheru 5y ago
      My opinion is pretty much worthless but I think this is a much more sensible approach, using the strengths of ML but putting constraints on the outputs. Just a simple question: to achieve a guarantee that those results are at least equal or better than the ones we have now on our battle tested setups, shouldn’t we use the same sampling we use on a “default” MRI? I mean… using those reconstruction algorithms to try to achieve a better result, without downsampling so that a standard reconstruction can still be performed to be checked against?
tangoed 5y ago
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.
- 01acheru 5y ago
  I totally agree with you on this one, but it is not the topic at hand. The article is not about using ML for feature detection, but on enhancing subsampled data. I actually find what you are saying it to be a much better usage of ML in this sector.

ryan93 5y ago

Why would the ML algorithm necessarily change the scan. The radiologist could still look at the unadulterated MRI.

lostlogin 5y ago
MRI is completely adulterated at every stage. Algorithms and filters make the final result palatable. The raw data is a k-space data file. It’s not really human readable (though you can spot noise spikes etc).
01acheru 5y ago
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.
- ryan93 5y ago
  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
  - 01acheru 5y ago
    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.
    - exdsq 5y ago
      Out of interest, do you understand how MRI reconstruction works?
      - 01acheru 5y ago
        If your question is literally “you understand how it works?” the answer is yes, I do. 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.
    - ryan93 5y ago
      Not obvious the human chosen function for reconstruction is necessarily better than the ML one. The human function doesn't save all the data either.
  - acerjio 5y ago
    A lot of the problem comes from the use of generative neural networks. If the prior is that the reconstructed images should "look" a certain way, then the algorithm will favor that. Some of our colleagues did early work with DL and got scared off of generative models due to finding issues with nonphysical results (read: broken layers of cortex in the brain, completely non-physical anatomy) that these models can generate from the undersampled raw data. 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.
- selimthegrim 5y ago
  There are definitely ways to work well with subsampled data, see Lester Mackey's recent work.
  - 01acheru 5y ago
    Could please share a link to the work you are referring to? I would really appreciate it (not ironically, it would be truly appreciated). I know we can work around subsampling, actually we have always been very good at it since our sampling data back in the day was way smaller than what we refer as subsampled today.
    - selimthegrim 5y ago
      http://stanford.edu/~lmackey/papers/thinning-slides.pdf http://stanford.edu/~lmackey/papers/thinning-slides.pdf
      - 01acheru 5y ago
        Thanks!

notarandomer 5y ago

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.

01acheru 5y ago
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 article comes from reading that someone telling about using generative models on health data, I don’t think is time for this yet.
- notarandomer 5y ago
  Thanks! I agree taht generative models on their own are definitely more risky than methods that combine ML for regularization along with data consistency terms that force the reconstructions to be consistent with the acquired data.
  - 01acheru 5y ago
    My opinion is pretty much worthless but I think this is a much more sensible approach, using the strengths of ML but putting constraints on the outputs. Just a simple question: to achieve a guarantee that those results are at least equal or better than the ones we have now on our battle tested setups, shouldn’t we use the same sampling we use on a “default” MRI? I mean… using those reconstruction algorithms to try to achieve a better result, without downsampling so that a standard reconstruction can still be performed to be checked against?

tangoed 5y ago

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.

01acheru 5y ago
I totally agree with you on this one, but it is not the topic at hand. The article is not about using ML for feature detection, but on enhancing subsampled data. I actually find what you are saying it to be a much better usage of ML in this sector.