Posted by
Kirsten Miles on
URL: http://confocal-microscopy-list.275.s1.nabble.com/Digital-imaging-ethics-as-pertaining-to-the-enhancement-of-microscopy-images-with-artificial-intellie-tp7588915p7588922.html
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I concur with Jeremy Adler, one safety, or "check" to AI-assisted microscopy
is to retain the original pre-processed image. As he states, some research
produces an unwieldy set of data, but more importantly in order for this to be
achievable a many-pronged approach needs to be taken. Support for data-retention
is still incomplete at best. Who is responsible for retention of current data,
of archiving data, especially for a scientist who has moved on, or of insuring
access to data? How many journals that accept data in image form have
instructive policies, and which are adhered to?
Attempts to routinize data integrity through software algorithms have never had
satisfactory results, and attempts to create standards have received little
formal support. Doug Cromey could address AI-assisted microscopy, but until a
set of standards is developed that includes original data retention (and money
to do so follows it) and more journals address the issue on their end, we'll
continue to fall behind as technology enables production without ethical
temperance.
Who is working on standards and digital ethics today?
Kirsten Miles
P.I. Outcomes434-960-5193
[hidden email]://scienceimageintegrity.org/
On Sun, Nov 18, 2018 2:16 PM, Jeremy Adler
[hidden email] wrote:
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A defence against over manipulation and dubious quantification is to make it an
absolute requirement that the original unprocessed images should be provided to
any interested party. On the only occasion I requested access to a published
image the authors refused and the journal upheld the right of the authors to
protect their published image from examination.
However there are difficulties with mandating access: Leica now offers immediate
deconvolution, meaning that the unprocessed images are not necessarily retained,
and some techniques can produce overwhelming volumes of data. A related issue is
who should be responsible for archiving data - authors, institutions or
journals.
Nonetheless the principle that original images should be accessible is an
important check.
Jeremy Adler
BioVis
Uppsala U
-----Original Message-----
From: Confocal Microscopy List <
[hidden email]> On Behalf Of
Andreas Bruckbauer
Sent: den 18 november 2018 12:55
To:
[hidden email]
Subject: Re: Digital imaging ethics as pertaining to the enhancement of
microscopy images with artificial intelligence
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Thanks John for bringing us up-to date on image processing, these are indeed
very important developments. I think there will be great changes coming over the
next years driven by the AI revolution in image and video processing. But the
fundamental limit should is that one cannot increase the information content of
an image beyond what was originally recorded. Of course the missing information
can be replaced by knowledge derived from other images. But then the new AI
algorithms will have similar flaws as human perception (optical illusions).
Science should be about measuring accurately instead of guessing.
My criticism of current publications and promotional videos in image processing
using AI is that they show the cases when the algorithms actually work well
(which might be most of the time), the important question is when do they fail
and produce the wrong results? With the fast development in the field, today's
problems are often solved a few days later with a new generation of the
algorithm, so detecting flaws in these algorithms is an ungrateful task. But I
think it is important and we will need to come up with very good quality control
standards to accept these results for medical imaging or scientific
publications. A few years ago I was very excited about using compressed sensing
in microscopy to "break the Nyquist barrier", but after looking into this in
more detail, I came to the conclusion that this only works well when images are
already heavily oversampled like in normal photography (more megapixels sell
better). When microscopy data is taken at the resolution limit there is not much
room for further compression. I would expect the same for neural network
approaches, works well when you have a lot of pixels and the information content
is not so high or accuracy is not so important. So the question is what is
actually necessary for a given experiment? If one wants to track some cells in a
fluorescence time laps movie, maybe noisy (low exposure) jpeg compressed data
combined with the latest AI algorithm trained on this problem is better than the
perfect exposure needed for current segmentation methods and raw data recording,
as in the latter case the cells might be killed a short time after the
experiment started by the higher light exposure.
best wishes
Andreas
-----Original Message-----
From: John Oreopoulos <
[hidden email]>
To: CONFOCALMICROSCOPY <
[hidden email]>
Sent: Sat, 17 Nov 2018 2:33
Subject: Digital imaging ethics as pertaining to the enhancement of microscopy
images with artificial intelligence
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Earlier today a few people (including myself) brought up Doug Cromey's excellent
treatise on digital imaging ethics in a related thread that dealt with training
new microscope users within a research setting. Lately I've been hearing a lot
about applications of machine learning and artificial intelligence to "improve",
"de-noise", or "fix" images (microscopy or otherwise), extracting new
information from low-resolution images, and even creating new 3D views of
samples with very little information. Here is just one such example from Nvidia
and MIT:
https://news.developer.nvidia.com/ai-can-now-fix-your-grainy-photos-by-only-looking-at-grainy-photos/
https://www.youtube.com/watch?time_continue=84&v=pp7HdI0-MIo
It's clear that the microscopy world will eventually come to a head with this
technology. I think I've seen a few research articles on this topic now, and
this month's issue of Nature Methods has a paper on this topic too:
https://www.nature.com/articles/s41592-018-0194-9
I've been wondering if and how Cromey's guide for digital imaging ethics should
be altered when it comes to AI-assisted microscope imaging. Should it be
allowed/accepted? Other readings of mine on AI show that machine learning
algorithms can produce biased results if the training datasets are incomplete in
some way, and the very nature of machine learning makes it difficult to
understand why it produced a certain result, since the deep learning neural
networks that are used to generate the results are essentially black boxes that
can't easily be probed. But on the other hand, I'm constantly blown away by what
I've seen so far online for other various applications of AI (facial
recognition, translation, etc.).
I also just finished a good read about AI from the perspective of economics:
https://www.predictionmachines.ai/
https://youtu.be/5G0PbwtiMJk
The basic message of this book is that AI makes prediction cheap. When something
is cheap, we use more of it. Other processes that complement prediction, like
judgement (by a human or otherwise) becomes more valuable. It's easy to see how
the lessons of this book could be re-framed for imaging science.
Curious to know the community's opinion on this matter. I used to laugh at the
following video, but now I'm not laughing:
https://www.youtube.com/watch?v=LhF_56SxrGk
John Oreopoulos
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