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Google's AR Microscope Quickly Highlights Cancer Cells (uploadvr.com)
An anonymous reader quotes a report from UploadVR: Google Research this week revealed an AR microscope (ARM) capable of detecting cancerous cells in real-time with the help of machine learning. Locating cancer with a standard microscope is a difficult and time-consuming process, with a raft of information for doctors to study and investigate. With this new solution, though, the microscope is able to quickly locate cancerous cells and then highlight them as a doctor peers inside. The platform uses a modified light microscope integrated with image analysis and machine learning algorithms into its field of view. An AR display sits above a camera that communicates with the algorithm to display data as soon as it locates an issue. In order words, the microscope immediately begins looking for cancerous cells as soon as you place a sample beneath it. It's effectively doing the same job as a doctor just, according to Google, a lot faster. Google posted a video about the AR microscope on YouTube.
honestly, much of what doctors do can be automated with machine learning. It's a broad subject with shallow depth. Machines are pretty good at taking care of living things, it turns out. This is a welcome solution to the "doctor shortage" in the USA.
One would use GOogle microscope for cancer screenings and then they would send you ads for cancer drugs and cancer centers?
Ads for hats when you lose your hair to cancer?
Is facebook gonna add a "like" button to it?
Hey, I'm just a bit cynical about these online advertising companies creating any of this tech.
needs better marketing because we've used stains and dyes to "augment" microscopes for at least a century. "Augmented Reality" sounds much cooler than "staining."
The state of medical care in the US is absolute garbage at best, so it is ripe for disruption.
To use Deep Machine Learning for detecting and actuating defective industrial images using an electron microscope.
This uses Augmented Reality for gray scale images instead of color images, and by the order of gigapixels and high-speed 3D cameras.
To imagine that there is a wide usage of this technology for industrial applications as repairing very ultra VLSI chips, micro mechanical things, MEMS, etc.
This is "think of the children" science. Pat on the back and all. But it's really just an advert for google. It looks suspiciously like image recognition with a different training set.
This is fiction.
Technician bursts into room, announcing: The tests were successful!
(Much cheering and applause)
Patient: Successful how?
Technician: The prototype can highlight cancer cells!
(More cheering, but louder this time)
Patient: Does this mean I have cancer?
Technician: (still ecstatic) YES! Do you want to see the video?
WARNING: Smartphones have side effects--most of them undocumented.
Make sure all you libtards band the Assault Rifle Microscope
"OMG google actually did something useful for once?"
Google Earth, Maps, Waze and Traffic not useful enough for you?
Ok, now they just need to add a pokeball to catch 'em all
This sounds like it would make a really interesting project, scaled down. Anyone know of a good hobbyist microscope with a movable stage that a USB camera would play nicely with? It would be fun to step the stage over X and Y for a slide of, say, pond water and create a classifier to determine what's in it.
"OMG google actually did something useful for once?"
Did you try googling that ?
Pain is merely failure leaving the body
This could make diagnosis actually worse.
Research from John Hopkins (I think?) has shown that performance of human doctor's DROPS when they are provided with automated results FIRST without a chance to view the un-annotated images. If they are provided with automated results in advance, they simply verify those results without doing much more looking themselves - the result being they wind up missing outliers that humans are good at seeing but machines aren't. It blinds them to outliers they would normally see.
If you do it the other way around though - have the doctor do their analysis FIRST, and THEN have those results double-checked by automated systems - you get super-additive performance better than either machine or doctor alone.
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