
Detecting Cancer Metastases on Gigapixel Pathology Images - signa11
https://drive.google.com/file/d/0B1T58bZ5vYa-QlR0QlJTa2dPWVk/view
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b_emery
Seems like pathology is a white collar job that could be replaced by AI. But
the pathologists I know are not worried about their jobs, (honestly, they
_are_ worried about it, but for other reasons - mostly changes in the US
healthcare system).

Its hard to imagine no human in the loop. My guess is that in the future,
there might be fewer pathologists, or may be the same number, just spending
time doing different things (perhaps reviewing results for tests we haven't
heard of yet) or just validating what the machine thinks. This being one of
the tools that makes them more productive.

Between the growth of medical technology and the aging population I don't
think they will every worry about finding work.

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rscho
Indeed, I do not think either that doctors need to worry much. AI will change
our jobs for the better, that is pretty sure. Maybe we will earn a little
less, but what of it?! Putting us out of work would mean handling the huge
amount of technical details involved in our daily jobs. People (and HN is no
exception to that) really, really underestimate that. The biggest problem is
the amount of rigorous feedback you need to train an efficient system. The
medical system is a long, long way from having such information retrieval
capabilities. And then you have the physical procedures... Robots are also
still have a long way to go on that one!

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austinjp
Unsurprisingly perhaps I'm not so sure. Could you elaborate on the processes
you allude to? I'd be very interested.

A pathologist I know attends cancer surgeries and analyses biopsies while the
surgeon waits to make a decision. Very little process there, but a fair amount
of time and hence cost and risk. A robot here might improve outcomes and
costs, and seems reasonably realistic. And the surgeon is not immune from the
threat to jobs posed by robots.

Another role I can imagine in pathology might be for a human to randomly spot-
check diagnoses made by image analysis. That would become comparatively very
mundane and potentially low paid. And since humans tend to perform worse than
AI in some image analysis tasks, I can envisage a time where the robots are
trusted 100% and the humans are seen as anachronistic.

In summary, I guess, complex processes might not protect jobs, but make them a
clear target for efficiency improvements.

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rscho
I am not saying medicine entails complex processes in terms of intellectual
capabilities. Quite the opposite, actually. The complexity I was speaking
about is that of actual physical procedures. Most specialties span a much
larger range of activities than one imagines at first glance. For example,
pathology does involve looking at surgical samples under the microscope, but
that is only the final step in the process. You first have to describe the
specimen macroscopically, and make sense of its original location and
orientation in the body. Autopsy, a process akin to surgery, also falls in the
realm of pathology. Granted, some specialties will be easier than others to
automate. But the complexity of the machine feedback processes that will have
to be implemented are currently overwhelming in most specialties. We will see
a long period during which AI facilitates our work, without replacing us, due
to lack of integration in the workplace. You mentioned surgery as difficult
for AI, but interestingly it is one of the fields where feedback will be
easier to implement, since patients rarely undergo surgery without extensive
imaging first. But in many technical procedures (anesthesia, intubation,
catheterization, bronchoscopy, etc...) you often perform without fixed
patterns, and orient yourself using the (quite variable) patient environment
and symptoms.

There is no question this all will be automated someday. But it is far more
complex than working on a car assembly line. We are not there yet.

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austinjp
Thanks for the reply. I didn't known that autopsy was a pathology discipline.

Personally I suspect that many medical roles will be broken into sub-roles and
semi automated. Humans will remain with stop/go decisions and overview, for a
while at least. I think we are probably in agreement.

Incidentally, I agree regarding surgery, perhaps I was unclear.

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camerintinbun
Related to this vaguely, I wonder how easy it would be to code a cell counting
app for a phone.

Awhile back, I designed and printed a case for my phone that could fit over
the lens of a microscope allowing me to take pictures/videos with it. One
technique that researchers use to estimate the number of cells in a
dish/suspension is to aliquot a small amount and place it on a hemocytometer.
Essentially, this is a slide with etched grids; counting the number of cells
in the area allows you to estimate the larger population. It is tedious and
depending on the number of cells, easy to mess up.

I'd imagine that phones now are powerful enough to analyze this. I don't know
whether a live-imaging setup is capable as I am no expert in phone tech. But
taking a picture, thresholding to remove noise, etc... seems like it could be
done.

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Stanleyc23
I dabble in computer vision. (e.g.
[http://git.io/fluidAR](http://git.io/fluidAR)) If you share some example
photos I could give it a try.

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nl
The blog post[1] is probably more approachable if you don't have a background
in both ML and cancer research.

[1] [https://research.googleblog.com/2017/03/assisting-
pathologis...](https://research.googleblog.com/2017/03/assisting-pathologists-
in-detecting.html)

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rml52
I was first author on the pigeon paper referenced below. It was a lot of fun
to do, but afterwards, we found that a convolutional neural net with transfer
learning could outperforscale interaction with them at the UCDH level. Already
there is interest in working with Chris Polage on his C. difficile activities.
m them, at least on mammogram images.

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rml52
Oops. Inadvertantly merged two messages below. See if you can figure out the
insertion sites...

