Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Machine Learning for MRI Image Reconstruction

rhotter.github.io

Post reply on HN
Print view View original

11–20 of 49 posts

Re: Machine Learning for MRI Image Reconstruction

#11

Post by halpert »

davidhyde wrote: post #6
olliej wrote: post #2

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

"Removing noise" is equivalent to adding detail.

Re: Machine Learning for MRI Image Reconstruction

#12

Post by 01acheru »

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

Re: Machine Learning for MRI Image Reconstruction

#13

Post by ryan93 »

01acheru wrote: post #12

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

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

Re: Machine Learning for MRI Image Reconstruction

#14

Post by csee »

exdsq wrote: post #10
csee wrote: post #7

Earlier quoted context omitted.

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.

That's true, but I don't see that as necessarily decisive. If 1 doctor and 1 engineer can achieve the bandwidth of 10 (more?) doctors on some specific scan, we're still talking about less intellectual capital and less training requirements overall.

Also I don't think we're talking about special hardware here. Couldn't we just have a software package produced by someone (university, company in a wealthier country) that is used by docs everywhere? Could be done without the need of a dedicated local engineer? Perhaps the WHO could approve certain software packages for universal use in very specific cases.

Re: Machine Learning for MRI Image Reconstruction

#15

Post by [deleted] »

csee wrote: post #14
exdsq wrote: post #10

Earlier quoted context omitted.

The issue is training people to use the machines and keeping them running, not even the doctors themselves.

That's true, but I don't see that as necessarily decisive. If 1 doctor and 1 engineer can achieve the bandwidth of 10 (more?) doctors on some specific scan, we're still talking about less intellectual capital and less training requirements overall. Also I don't think we're talking about special hardware here. Couldn't we just have a software package produced by someone (university, company in a wealthier country) tha…

[deleted]

Re: Machine Learning for MRI Image Reconstruction

#16

Post by 01acheru »

ryan93 wrote: post #13
01acheru wrote: post #12

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

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

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.

Re: Machine Learning for MRI Image Reconstruction

#17

Post by ryan93 »

01acheru wrote: post #16
ryan93 wrote: post #13

Earlier quoted context omitted.

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

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

Re: Machine Learning for MRI Image Reconstruction

#18

Post by HPsquared »

tqi wrote: post #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 unexpe…

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)

Re: Machine Learning for MRI Image Reconstruction

#19

Post by 01acheru »

ryan93 wrote: post #17
01acheru wrote: post #16

Earlier quoted context omitted.

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

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.

Re: Machine Learning for MRI Image Reconstruction

#20

Post by olliej »

davidhyde wrote: post #6
olliej wrote: post #2

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

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
Post reply on HN