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FastMRI leverages adversarial training to remove image artifacts

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Re: FastMRI leverages adversarial training to remove image artifacts

#31

Post by ebg13 »

A lot of people here are rightly concerned about the dangers of falsely marking something as an artifact, but let me present additional data that will hopefully sway you a little bit...

If you need an MRI or a CT of an area adjacent to orthopedic implants, you are currently 100% SOL because distortion or reflection artifacts from the metal completely destroy the imagery across a medically significant distance. There are computational filtering techniques for reducing these artifacts, but, respectfully, they are still really terrible, and close to the implants you can't see shit. All advancements in this area short of inventing new imaging physics will most likely be purely computational corrections. Consider that.

Re: FastMRI leverages adversarial training to remove image artifacts

#32

Post by mikeortman »

voicedYoda wrote: post #25

Facebook has absolutely no reason to be doing work with healthcare. Sure they have great computing power and top engineering talent to figure out how to sell more ads, but the trade-off for any educational facility to freely hand over medical data (de-identified or not) is wreckless.

What till you find out about the Google / Ascension partnership.

I trust seasoned talent being paid hundreds of thousands of dollars a year in partnership with equally well paid healthcare professionals over PhD students scraping by on grant dollars, keeping their code and datasets in a private GitHub repo that will never see the light of day except for a citation in research papers of other scholars.

Not trying to be mean, but if Facebook is trying to fix their moral compass with dollars, go for it.

Re: FastMRI leverages adversarial training to remove image artifacts

#33

Post by vardump »

ebg13 wrote: post #31

A lot of people here are rightly concerned about the dangers of falsely marking something as an artifact, but let me present additional data that will hopefully sway you a little bit... If you need an MRI or a CT of an area adjacent to orthopedic implants, you are currently 100% SOL because distortion or reflection artifacts from the metal completely destroy the imagery across a medically significant distance. There…

I think I'd prefer radiologists use both computationally filtered and this. Computational filtering has also advanced over the years.

Re: FastMRI leverages adversarial training to remove image artifacts

#34

Post by DataDrivenMD »

A physician's $0.02 - The clinical relevance of FB's work is clearly stated in the blog post: "While state-of-the-art facilities today use 3 Tesla MRI machines, scanners with lower-strength magnets (1.5 Tesla, for example) are still commonly used around the world." Considering that a 1.5T MRI machine costs about $1M less than a comparable 3T model (+/- the cost of warranty, support, and installation), FB's work in this area has the potential to make a BIG positive impact on the lives of millions of patients. Which is why I will be cheering them on.

If they reproduce their results in other clinical settings, the immediate impact on patient care includes: 1) accelerating diagnosis (and treatment) for patients with traumatic brain injuries (by effectively up-scaling lower resolution scans) 2) healthcare providers in developing countries will effectively get a low-cost "upgrade" to their existing equipment 3) cancer patients in rural America could be monitored for treatment response in a setting that is closer to home (because rural communities tend to be resource-poor in terms of medical technology).

If we consider that a logical extension of their work could be to develop a compression algorithm for MRI data, then it's easy to see an even broader impact that includes: 1) connecting rural patients with high-quality radiologist services (i.e. remote MRI interpretations), and 2) decrease the cost of long-term storage, access, and retrieval for MRI data.

On the topic of FB's issues with privacy: I agree that FB has a long way to earn my trust as a doctor and a patient. That being said, it's important to give credit where credit is due. It seems that FB gained access to the imaging data by working collaboratively with NYU on this specific project. By comparison, it's an open secret among those of us in the biomedical informatics community that over the course of many years Google Cloud has quietly gained access to the personal health information of millions of Americans. So, when it comes to privacy concerns, it's important to avoid being myopic - the concern is valid, but the primary threat may not be as obvious as it first seems.

Re: FastMRI leverages adversarial training to remove image artifacts

#35

Post by p1necone »

mikeortman wrote: post #28

Please don’t make sweeping, generalizing opinions on the implications of the work. It’s a subjective problem to solve, so if are not a radiologist who has first-hand experience with this issue, stop. Here are the results from the paper: The radiologists ranked our adversarial approach as better than the standard and dithering approaches with an aver- age rank of 2.83 out of a possible 3. This result is statisti- call…

Intuitively I don't see that there's much value in asking radiologists to subjectively "rank" the images. Surely the thing that needs to be tested here is patient outcomes?

Re: FastMRI leverages adversarial training to remove image artifacts

#36

Post by ska »

Brakenshire wrote: post #16
est31 wrote: post #8

I'm no fan of this. What if it treats a tumor as an artifact? This reminds me of the xerox scandal about broken OCR that erroneously deduplicated parts of images that had different contents. This module might work well, but the modules by cheap competitors might have such behaviour, and it's extremely hard to test that an implementation is bug free.

Is it extremely difficult? I’d have thought quantifying the error rate for a particular application would be relatively easy.

It’s harder than it looks in this case.

Re: FastMRI leverages adversarial training to remove image artifacts

#37

Post by lostlogin »

ska wrote: post #30
lostlogin wrote: post #22

Earlier quoted context omitted.

Digital and computed radiography are quite poor examples of progress though, as the resolution is worse and the radiation dose was higher than film radiography. This may have changed in the last few years but was strikingly true at the outset. The advantages they gave were in every other way (physical storage, availability, duplication, speed at which they could be accessed etc).

The point I was trying to make has nothing to do with image quality. The issue was, radiologist had to deal with a choice of different post-processing of this data. The processing they said they liked best (somewhat consistently) was not the processing that they performed best on, empirically (somewhat consistently). This is related to the issue of evaluating the value of ML post processing, we could see a similar ef…

Thanks - I missed your point. Image quality in MR is very much a moving target too as it varies between patients and there is a far bit of variation in practice. Scans are speed up or slowed down for a variety of reasons. Making a scan faster to fit in another patient or any number of other reasons is something that happens regularly.

Re: FastMRI leverages adversarial training to remove image artifacts

#38

Post by ineedasername »

est31 wrote: post #8

I'm no fan of this. What if it treats a tumor as an artifact? This reminds me of the xerox scandal about broken OCR that erroneously deduplicated parts of images that had different contents. This module might work well, but the modules by cheap competitors might have such behaviour, and it's extremely hard to test that an implementation is bug free.

The Xerox OCR problem is exactly what came to mind after reading the first few sentences t of the article. And that problem happened well after the times when OCR of standard text had been considered a "difficult" problem. That said, I'm not against using this sort of development, I just think they need to be treated with skepticism and constantly evaluated. If deployed widely, some percentage of scans should always be evaluated from a QA perspective to always be vigilant of misclassification, drift, etc.

Re: FastMRI leverages adversarial training to remove image artifacts

#39

Post by ebg13 »

vardump wrote: post #33
ebg13 wrote: post #31

A lot of people here are rightly concerned about the dangers of falsely marking something as an artifact, but let me present additional data that will hopefully sway you a little bit... If you need an MRI or a CT of an area adjacent to orthopedic implants, you are currently 100% SOL because distortion or reflection artifacts from the metal completely destroy the imagery across a medically significant distance. There…

I think I'd prefer radiologists use both computationally filtered and this. Computational filtering has also advanced over the years.

This _is_ computational filtering. It's not philosophically any different. Every filtering method algorithmically guesses what's important or what's real and what's not.

Re: FastMRI leverages adversarial training to remove image artifacts

#40

Post by zn473 »

deepnotderp wrote: post #23

This is a bad idea, neural nets upscale by "hallucinating" in the details. That's fine for videos for entertainment, not for medical imaging. And this is distinctly different from compressed sensing which uses a high frequency and mathematical basis.

I went to a medical imaging workshop recently, and the consensus was that deep learning approaches will completely replace classical compressed sensing. They are using the same principles of acquiring randomized samples, so it's still compressed sensing, they just produce dramatically better results than classical CS techniques.
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