5 ms·

Nice work! However, a cautionary tale on AI medical image "denoising": (and beyond, in science) - See the artifacts? The algorithm plugs into ambiguous area

Nice work! However, a cautionary tale on AI medical image "denoising": (and beyond, in science) - See the artifacts? The algorithm plugs into ambiguous areas of the image stuff it has seen before / it was trained with. So, if such a system was to "denoise" (or compress, which - if you think about it - is basically the same operation) CT scans, X-rays, MRIs, etc., in ambiguous areas it could plug-in diseased tissue where the ground-truth was actually healthy. Or the opposite, which is even worse: substitute diseased areas of the scan with healthy looking imagery it had been trained on. Reading recent publications that try to do "denoising" or resolution "enhancement" in medical imaging contexts, the authors seem to be completely oblivious to this pitfall. (maybe they had a background as World Bank / IMF economists?)

theemathas 4y ago
Here's a similar case of a scanner using a traditional compression algorithm. It has a bug in the compression algorithm, which made it replace a number in the scanned image with a different number. https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_are_switching_written_numbers_when_scanning https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...
- sgc 4y ago
  That is completely outside all my expectations prior to reading it. The consequences are potentially life and death, or incarceration, etc, and yet they did nothing until called out and basically forced to act. A good reminder that the bug can be anywhere, and when things stop working we often need to get very dumb, and just methodically troubleshoot.
  - function_seven 4y ago
    We programmers tend to think our abstractions match reality somehow. Or that they don't leak. Or even if they do leak, that leakage won't spill down several layers of abstraction. I used to install T1 lines a long time ago. One day we had a customer that complained that their T1 was dropping every afternoon. We ran tests on the line for extended periods of time trying to troubleshoot the problem. Every test passed. Not a single bit error no matter what test pattern we used. We monitored it while they used it and saw not a single error, except for when the line completely dropped. We replaced the NIU card, no change. Customer then hit us with, "it looks like it only happens when Jim VNCs to our remote server". Obviously a userland program (VNC) could not possibly cause our NIU to reboot, right?? It's several layers "up the stack" from the physical equipment sending the DS1 signal over the copper. But that's what it was. We reliably triggered the issue by running VNC on their network. We ended up changing the NIU and corresponding CO card to a different manufacturer (from Adtran to Soneplex I think?) to fix the issue. I wish I had had time to really dig into that one, because obviously other customers used VNC with no issues. Adtran was our typical setup. Nothing else was weird about this standard T1 install. But somehow the combination of our equipment, their networking gear, and that program on that workstation caused the local loop equipment to lose its mind. This number-swapping story hit me the same way. We would all expect a compression bug to manifest as blurry text, or weird artifacts. We would never suspect a clean substitution of a meaningful symbol in what is "just a raster image".
    - jend 4y ago
      Reminds me of this story: http://blog.krisk.org/2013/02/packets-of-death.html http://blog.krisk.org/2013/02/packets-of-death.html tldr: Specific packet content triggers a bug in the firmware of an Intel network card and bricks it until powered off.
- caycep 4y ago
  I assume something like jpeg (used in the DICOM standard today) has more eyes on the code than proprietary Xerox stuff? hopefully at least... I have seen weird artifacts on MRI scans, specifically the FLAIR image enhancement algorithm used on T2 images, i.e. white spots, which could in theory be interpreted by a radiology as small strokes or MS...so I always take what I see with a grain of salt..
  - ska 4y ago
    The DICOM standard stuff did have a lot of eyes on it, and was tuned toward fidelity which helps. It's not perfect, but what is. MRI artifacts though are a whole can of worms, but fundamentally most of them come from a combination of the EM physics involved, and the reconstruction algorithm needed to produce an image from the frequency data. I'm not sure what you mean by "image enhancement algorithm"; FLAIR is a pulse sequence used to suppress certain fluid signals, typically used in spine and brain. Many of the bright spots you see in FLAIR are due to B1 inhomogeneity, iirc (it's been a while though)
  - ska 4y ago
    Probably worth mentioning also that "used in DICOM standard" is true but possibly misleading to someone unfamiliar with it. DICOM is a vast standard. In it's many crevasses, it contains wire and file encoding schemas, some of which include (many different) image data types, some of which allow (multiple) compression schemes, both lossy and lossless, as well as metadata schemes. These include JPEG, JPEG-LS, JPEG-2000, MPEG2/4, HVEC. I think you have to encode the compression ratio as well, if you do lossy compression. You definitely have to note that you did lossy compression.
  - jahewson 4y ago
    The xerox problem wasn’t an issue with the standard, it was a bug in the code.
- SV_BubbleTime 4y ago
  Iirc, this was an issue or conspiracy fuel or whatever with the birth certificate that Obama released. That some of the unique elements in the scan repeated over and over.
adammarples 4y ago
Mentioned in TFA at least twice
Der_Einzige 4y ago
Sounds like you need lossless compression. I was told that the GPT-2 text compression variant was a lossless compressor (https://bellard.org/libnc/gpt2tc.html https://bellard.org/libnc/gpt2tc.html), why is stable diffusion lossy?
- operator-name 4y ago
  Probably something to do with the variational auto encoder, which is lossy.
ska 4y ago
People have been publishing fairly useless papers "for" medical imaging enhancement/improvement for 3+ decades now. NB this is not universal (there are some good ones) and not limited to AI techniques, although essentially every AI technique that comes along gets applied to compression/denoising/"superres"/etc. if it can, eventually. The main problems is that that typical imaging researchers are too far from actual clinical applications, and often trying to solve the wrong problems. It's a structural problem with academic and clinical incentives, as much as anything else.
fny 4y ago
There is nothing in the article suggesting this should be used for medical imaging.
petesergeant 4y ago
From the article: > a bit of a danger of this method: One must not be fooled by the quality of the reconstructed features — the content may be affected by compression artifacts, even if it looks very clear ... plus an excellent image showing the algorithm straight making stuff up, so I suspect the author is aware.
gregw134 4y ago
Fun to imagine this could show up in future court cases. Is the picture true, or were details changed by the ai compression algorithm?
- layer8 4y ago
  This is similar to whether you can trust eye witnesses’ recollections. Brains do their own “compression” of what they observed.
- im3w1l 4y ago
  This has already happened. Pinch-to-zoom became a subject of controversy in the Kyle Rittenhouse trial because of a fear that it could introduce false details.
anarticle 4y ago
In my xp, medical imaging at the diagnostic tier uses only lossless (JPEG2000 et al). It was explicitly mentioned on our SOP/policies that we had to have a lossless setup. Very sketchy to use a super resolution for diagnostics. In research (flourescence), sure. ref: my direct experience of pathology slide scanning machines and their setup.
yshrestha 4y ago
That is a pretty scary side effect and why AI in medicine needs to be handled carefully, especially anything with generative capability. I do think, however, the primary application of these newer generation of generative models will be useful for more sophisticated data augmentation. For example, a lung nodule detection AI trained on a combination of synthetic and real lung nodules may perform better than one trained on real data alone. Do you think there are any other responsible applications?
im3w1l 4y ago
I think this could be avoided by using some simple-stupid metric for measuring the difference between the compressed and uncompressed images and rejecting the compressed one if it's too different. Maybe something like dividing the image into small squares, computing the L2 difference between original and compressed. If any square has too large L2 difference then compression failed. Th metric might need to be informed by the domain. Edit: Or maybe PSNR+SSIM?

theemathas 4y ago

Here's a similar case of a scanner using a traditional compression algorithm. It has a bug in the compression algorithm, which made it replace a number in the scanned image with a different number. https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_are_switching_written_numbers_when_scanning https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...

sgc 4y ago
That is completely outside all my expectations prior to reading it. The consequences are potentially life and death, or incarceration, etc, and yet they did nothing until called out and basically forced to act. A good reminder that the bug can be anywhere, and when things stop working we often need to get very dumb, and just methodically troubleshoot.
- function_seven 4y ago
  We programmers tend to think our abstractions match reality somehow. Or that they don't leak. Or even if they do leak, that leakage won't spill down several layers of abstraction. I used to install T1 lines a long time ago. One day we had a customer that complained that their T1 was dropping every afternoon. We ran tests on the line for extended periods of time trying to troubleshoot the problem. Every test passed. Not a single bit error no matter what test pattern we used. We monitored it while they used it and saw not a single error, except for when the line completely dropped. We replaced the NIU card, no change. Customer then hit us with, "it looks like it only happens when Jim VNCs to our remote server". Obviously a userland program (VNC) could not possibly cause our NIU to reboot, right?? It's several layers "up the stack" from the physical equipment sending the DS1 signal over the copper. But that's what it was. We reliably triggered the issue by running VNC on their network. We ended up changing the NIU and corresponding CO card to a different manufacturer (from Adtran to Soneplex I think?) to fix the issue. I wish I had had time to really dig into that one, because obviously other customers used VNC with no issues. Adtran was our typical setup. Nothing else was weird about this standard T1 install. But somehow the combination of our equipment, their networking gear, and that program on that workstation caused the local loop equipment to lose its mind. This number-swapping story hit me the same way. We would all expect a compression bug to manifest as blurry text, or weird artifacts. We would never suspect a clean substitution of a meaningful symbol in what is "just a raster image".
  - jend 4y ago
    Reminds me of this story: http://blog.krisk.org/2013/02/packets-of-death.html http://blog.krisk.org/2013/02/packets-of-death.html tldr: Specific packet content triggers a bug in the firmware of an Intel network card and bricks it until powered off.
caycep 4y ago
I assume something like jpeg (used in the DICOM standard today) has more eyes on the code than proprietary Xerox stuff? hopefully at least... I have seen weird artifacts on MRI scans, specifically the FLAIR image enhancement algorithm used on T2 images, i.e. white spots, which could in theory be interpreted by a radiology as small strokes or MS...so I always take what I see with a grain of salt..
- ska 4y ago
  The DICOM standard stuff did have a lot of eyes on it, and was tuned toward fidelity which helps. It's not perfect, but what is. MRI artifacts though are a whole can of worms, but fundamentally most of them come from a combination of the EM physics involved, and the reconstruction algorithm needed to produce an image from the frequency data. I'm not sure what you mean by "image enhancement algorithm"; FLAIR is a pulse sequence used to suppress certain fluid signals, typically used in spine and brain. Many of the bright spots you see in FLAIR are due to B1 inhomogeneity, iirc (it's been a while though)
- ska 4y ago
  Probably worth mentioning also that "used in DICOM standard" is true but possibly misleading to someone unfamiliar with it. DICOM is a vast standard. In it's many crevasses, it contains wire and file encoding schemas, some of which include (many different) image data types, some of which allow (multiple) compression schemes, both lossy and lossless, as well as metadata schemes. These include JPEG, JPEG-LS, JPEG-2000, MPEG2/4, HVEC. I think you have to encode the compression ratio as well, if you do lossy compression. You definitely have to note that you did lossy compression.
- jahewson 4y ago
  The xerox problem wasn’t an issue with the standard, it was a bug in the code.
SV_BubbleTime 4y ago
Iirc, this was an issue or conspiracy fuel or whatever with the birth certificate that Obama released. That some of the unique elements in the scan repeated over and over.

adammarples 4y ago

Mentioned in TFA at least twice

Der_Einzige 4y ago

Sounds like you need lossless compression. I was told that the GPT-2 text compression variant was a lossless compressor (https://bellard.org/libnc/gpt2tc.html https://bellard.org/libnc/gpt2tc.html), why is stable diffusion lossy?

operator-name 4y ago
Probably something to do with the variational auto encoder, which is lossy.

ska 4y ago

People have been publishing fairly useless papers "for" medical imaging enhancement/improvement for 3+ decades now. NB this is not universal (there are some good ones) and not limited to AI techniques, although essentially every AI technique that comes along gets applied to compression/denoising/"superres"/etc. if it can, eventually. The main problems is that that typical imaging researchers are too far from actual clinical applications, and often trying to solve the wrong problems. It's a structural problem with academic and clinical incentives, as much as anything else.

fny 4y ago

There is nothing in the article suggesting this should be used for medical imaging.

petesergeant 4y ago

From the article: > a bit of a danger of this method: One must not be fooled by the quality of the reconstructed features — the content may be affected by compression artifacts, even if it looks very clear ... plus an excellent image showing the algorithm straight making stuff up, so I suspect the author is aware.

gregw134 4y ago

Fun to imagine this could show up in future court cases. Is the picture true, or were details changed by the ai compression algorithm?

layer8 4y ago
This is similar to whether you can trust eye witnesses’ recollections. Brains do their own “compression” of what they observed.
im3w1l 4y ago
This has already happened. Pinch-to-zoom became a subject of controversy in the Kyle Rittenhouse trial because of a fear that it could introduce false details.

anarticle 4y ago

In my xp, medical imaging at the diagnostic tier uses only lossless (JPEG2000 et al). It was explicitly mentioned on our SOP/policies that we had to have a lossless setup. Very sketchy to use a super resolution for diagnostics. In research (flourescence), sure. ref: my direct experience of pathology slide scanning machines and their setup.

yshrestha 4y ago

That is a pretty scary side effect and why AI in medicine needs to be handled carefully, especially anything with generative capability. I do think, however, the primary application of these newer generation of generative models will be useful for more sophisticated data augmentation. For example, a lung nodule detection AI trained on a combination of synthetic and real lung nodules may perform better than one trained on real data alone. Do you think there are any other responsible applications?

im3w1l 4y ago

I think this could be avoided by using some simple-stupid metric for measuring the difference between the compressed and uncompressed images and rejecting the compressed one if it's too different. Maybe something like dividing the image into small squares, computing the L2 difference between original and compressed. If any square has too large L2 difference then compression failed. Th metric might need to be informed by the domain. Edit: Or maybe PSNR+SSIM?