
An Augmented Reality Microscope for Cancer Detection - kbyatnal
https://research.googleblog.com/2018/04/an-augmented-reality-microscope.html
======
cstavish
Those familiar with the field will recognize an augmented reality microscope
as an incomplete alternative to, or stepping-stone towards AI applied to
whole-slide images, which are multi-resolution images of more or less
identical quality quality to that of a microscope.

For some use-cases, deploying AI to microscopic fields of view is a viable,
lower-cost alternative to creating whole-slide images and running AI on them
in their entirety (whole-slide scanners are a bit expensive). Specifically, if
a pathologists identifies a suspicious region, the augmented scope can provide
useful support. However, many types of anatomic pathology assessment require
laborious review of several slides. Only AI applied to whole-slide images can
pre-identify rare events or "hot spots", saving pathologist time while
improving diagnostic confidence.

~~~
jsolson
> (whole-slide scanners are a bit expensive)

Huh... Why? It _seems_ like a problem with an easy (and cheap) solution, given
how readily available (and cheap!) suitably precise cartesian bots are these
days, combined with high-quality digital microscopes and the state of modern
image alignment algorithms.

What am I missing here?

~~~
dpeckett
A lot of that cost comes from all the supporting hardware and certified
software. Plus a lot of the machines have to be designed for high throughput
applications.

------
j32fun
Much of Radiology and Pathology specimen interpretation is based on reliable
and consistent detection. To come and think of it, the application of AI into
this area is fantastic because it removes human fatigue and missed diagnoses.
AI neural nets seem well equipped for image recognition. At the very least,
this can lead to a first level flagging of specimens.

My only concern is that with any system, a major downside would be that human
operators would place too much reliance on a system that works great _most of
the time_ , resulting in missing something that otherwise would have been
caught. This happens every once in a while, such as with EKG machines spitting
out a diagnosis based on electrical activity patterns.

------
bookofjoe
Paper:
[https://drive.google.com/file/d/1WRBCqJItaGly-9PDSMlwQ5Ldhc8...](https://drive.google.com/file/d/1WRBCqJItaGly-9PDSMlwQ5Ldhc8lB0lf/view)

------
dekhn
There are some neat ideas in here. I really like the cheap "current lens
detector" that uses a webcam to see a simple color fiducial.

------
aaavl2821
This seems to be very interesting from a technical perspective, but it seems
that it would not improve upon the accuracy of current tools and would be
additive to cost, if only incrementally so. Thus the real world benefits would
be reducing time spent reviewing slides or other workflow improvements?

~~~
nickparker
Layperson speaking, but I believe skilled labor is most of the cost of these
diagnostics. Even if the scope is $100k, the doctor is $200k/yr so it'd make
sense for this to be a cost reducing tool if it increases speed.

------
claar
This sounds fantastic. It seems to follow that eventually computers will be
able to analyze vastly more tissue/cells than humans are capable of.

------
amelius
It's always nice to read about new applications, but using DL to build image
classifiers is not very impressive in 2018.

I would be more impressed if they put the data out in the open, and let
everybody compete on building a better classifier.

------
beluis3d
This seems more like Computer Vision than augmented reality.

