Machine Learning for MRI Image Reconstruction
rhotter.github.io
Machine Learning for MRI Image Reconstruction
1–10 of 49 posts
Re: Machine Learning for MRI Image Reconstruction
#2Things 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.
Re: Machine Learning for MRI Image Reconstruction
#3> 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.
Re: Machine Learning for MRI Image Reconstruction
#4> 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 unexpe…
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.
Re: Machine Learning for MRI Image Reconstruction
#5> 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 unexpe…
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.
Those countries are lacking basic healthcare standards and infrastructure. I doubt that lack of diagnosis due to understaffing is the bottleneck.
Re: Machine Learning for MRI Image Reconstruction
#6Things 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
Re: Machine Learning for MRI Image Reconstruction
#7Earlier quoted context omitted.
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.
Those countries are lacking basic healthcare standards and infrastructure. I doubt that lack of diagnosis due to understaffing is the bottleneck.
Countries with a GDP per capita of $5,000-$10,000 typically do have good medical care in private, but most of the population is excluded because of cost. If we give the doctors ML tools to increase bandwidth, then that should help the situation by increasing supply. Suppose we could 10x the bandwidth for routine scans. The cost should go down in private, public health capacity will go up, and overall more people should be able to access it.
Re: Machine Learning for MRI Image Reconstruction
#8Earlier quoted context omitted.
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.
Those countries are lacking basic healthcare standards and infrastructure. I doubt that lack of diagnosis due to understaffing is the bottleneck.
It often still is a good idea to have an approach for a possible second step for when the first step or steps are established. A lot of ifs and sometimes countries entirely skip kinds of technology like the landline phones in Africa and go to mobile phones directly since it was easier to establish. Maybe not-so-wealthy countries will see an entirely different kind of health care in 10 years than we know it now.
Re: Machine Learning for MRI Image Reconstruction
#9> 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 unexpe…
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.
>80% ML + 20% human is better than no medical care at all.
That implies that doing something is always better than doing nothing. Unless we're talking things like antibiotics, I'm not sure I'd agree. Medical error is a nontrivial cause of death, increasing that significantly could probably be worse than what you're trying to treat.
Re: Machine Learning for MRI Image Reconstruction
#10Earlier quoted context omitted.
Those countries are lacking basic healthcare standards and infrastructure. I doubt that lack of diagnosis due to understaffing is the bottleneck.
Countries with a GDP per capita of $5,000-$10,000 typically do have good medical care in private, but most of the population is excluded because of cost. If we give the doctors ML tools to increase bandwidth, then that should help the situation by increasing supply. Suppose we could 10x the bandwidth for routine scans. The cost should go down in private, public health capacity will go up, and overall more people shou…
The issue is training people to use the machines and keeping them running, not even the doctors themselves.