http://confocal-microscopy-list.275.s1.nabble.com/Digital-imaging-ethics-as-pertaining-to-the-enhancement-of-microscopy-images-with-artificial-intellie-tp7588915p7588927.html
this planet... :-)
And in remembrance of Philip K. Dick:
> *****
> To join, leave or search the confocal microscopy listserv, go to:
>
http://lists.umn.edu/cgi-bin/wa?A0=confocalmicroscopy> Post images on
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> *****
>
> Ralf, AI will one day be able to discover the “unknown” just as humans do, knowing their field, looking out for things which do not fit into the current theoretical framework, forming a new hypothesis, doing experiments and modifying the hypothesis.... the robot scientist is waiting to replace us, obviously there is long way to go
>
http://aiweirdness.com/post/172894792687/when-algorithms-surprise-us>
> Andreas
>
>
>
> Sent from my phone
>
>> On 19 Nov 2018, at 10:59, Ralf Palmisano OICE <
[hidden email]> wrote:
>>
>> *****
>> To join, leave or search the confocal microscopy listserv, go to:
>>
http://lists.umn.edu/cgi-bin/wa?A0=confocalmicroscopy>> Post images on
http://www.imgur.com and include the link in your posting.
>> *****
>>
>> Benjamin, thanks for pointing out all this which I fully agree with!
>>
>> One more from my side.
>> How can I ever trust AI if I am exploring terra incognita by imaging, which quite often is the case, that's why quite a few of us are doing imaging.
>> How could (whatever background) AI ever produce data that are not known yet. How could IT know what is unknown???
>>
>> Cheers Ralf
>>
>>> Am 18.11.2018 um 23:19 schrieb Benjamin Smith:
>>> *****
>>> To join, leave or search the confocal microscopy listserv, go to:
>>>
http://lists.umn.edu/cgi-bin/wa?A0=confocalmicroscopy>>> Post images on
http://www.imgur.com and include the link in your posting.
>>> *****
>>>
>>> Here are my two cents, I think the simplest rubric for using an AI
>>> algorithm in science is, "Have I ever seen a paper where this was done by
>>> hand?"
>>>
>>> For example, tracking objects, identifying subsets of objects, counting
>>> objects are all the types of image processing tasks that have been done by
>>> hand, and therefore are excellent candidates for AI. The reason these are
>>> valid is that a human can look at the data and immediately ascertain the
>>> result, but the challenge is in coming up with a computational algorithm
>>> that will robustly define the problem as clearly as we are seeing it.
>>> Whenever I teach people computational image processing, I always point out
>>> that there are certain tasks that are trivial for computers but are
>>> non-trivial for humans (convolutions, counting and measuring thousands of
>>> objects, measuring distances between all pairs of objects, etc.) and tasks
>>> that are often trivial for humans but non-trivial for computers (image
>>> segmentation in a complex feature space, tracking objects in a 2D image
>>> that can cross in 3D space, etc.), and therefore sometimes the best
>>> solution is to write an algorithm that lets the human do the things they
>>> are good at, and the computer does the rest.
>>>
>>> To me, the goal of AI in image processing is to replicate what we as
>>> researchers would have done anyway, just in a manner where the computer
>>> does it on its own. For example, using a well designed convolutional
>>> neural network to learn to segment an image is an excellent application.
>>> You could find an segmentation algorithm by hand, tuning bandpass filters,
>>> trying different convolutions, adjusting both the magnitude and order of
>>> these parameters to get to some lowest energy state. With convolutional
>>> neural networks, computers can effectively perform this exact same task,
>>> and even in relatively the same manner that we would have, allowing us to
>>> go do something else. On top of that, they can search a much broader
>>> parameter space than anyone would ever want to do by hand, and therefore
>>> possibly come up with an even more robust algorithm than we could have.
>>> Therefore, if it will likely be faster to build a training dataset than to
>>> try and explore the entire parameter space needed to segment an image, AI
>>> is a good candidate for these tasks.
>>>
>>> Using the same rubric I mentioned at the start, I have never seen a paper
>>> where someone showed an artist a bunch of high resolution images, and then
>>> asked them to draw high resolution interpretations of low resolution
>>> images, and then publishing meaningful conclusions from this (for example,
>>> scientific conclusions should not be drawn from artistic renderings of
>>> exoplanets). The reason we don't do this is that the task is premised on
>>> the assumption that the low resolution images have the same feature space
>>> as the high resolution ones. Therefore, the only case where the assumption
>>> that the artist's renderings are correct are if our low resolution images
>>> have the same information as the high resolution ones, meaning the low res
>>> images are completely redundant and useless in the sense that they offer no
>>> additional information.
>>>
>>> Along these lines, asking a neural network to draw in features in images
>>> based on previous images is not science, because you are forcing the
>>> assumption that all the data matches the hypothesis. Google's DeepDream is
>>> an excellent case of this, where depending on what the algorithm was
>>> trained on, it will then put those features into any other image. This is
>>> a great video explaining the impact of using different network layers in
>>> image reconstruction:
https://www.youtube.com/watch?v=BsSmBPmPeYQ>>>
>>> Therefore, if you use a neural network in image reconstruction, what you
>>> are really doing is having a computer or artist to draw a high resolution
>>> version of your image that matches your hypothesis. While this is
>>> inherently not science (as you are changing the data to match your
>>> hypothesis, rather than the other way around), this type of image
>>> reconstruction still has a place in medical science. For example, a human
>>> body does have a well defined and constrained feature space. As such, if
>>> you took a low-res CT scan of a human, reconstruction that image based on a
>>> training set of a human would create a meaningful image that could guide
>>> diagnoses, and allow for significantly lower X-ray exposure to the
>>> patient. Therefore, while AI image reconstruction in science appears to be
>>> a case of circular logic, in medicine it can have very meaningful
>>> applications.
>>>
>>> Just my own to cents, and looking forward to hearing other people's
>>> insights,
>>> Ben Smith
>>>
>>> On Sun, Nov 18, 2018 at 3:55 AM Andreas Bruckbauer <
>>>
[hidden email]> wrote:
>>>
>>>> *****
>>>> To join, leave or search the confocal microscopy listserv, go to:
>>>>
http://lists.umn.edu/cgi-bin/wa?A0=confocalmicroscopy>>>> Post images on
http://www.imgur.com and include the link in your posting.
>>>> *****
>>>>
>>>> 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
>>>>
>>>> *****
>>>> To join, leave or search the confocal microscopy listserv, go to:
>>>>
http://lists.umn.edu/cgi-bin/wa?A0=confocalmicroscopy>>>> Post images on
http://www.imgur.com and include the link in your posting.
>>>> *****
>>>>
>>>> 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
>>>>
>> --
>> Ralf Palmisano
>> Head - Optical Imaging Centre Erlangen
>>
>> Fellow Royal Microscopical Society
>> Member Royal Society of Medicine
>>
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>> Board of Directors Core Technologies for Life Science
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