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For the last few years we’ve worked with the National Stereoscopic Association to support the 3D Digital Showcase photo competition featured at the NSA’s annual conventions. The images from this past year’s showcase are now live for everyone to view. We really enjoy the diversity of images submitted by 3D artists and enthusiasts to this event, and this gallery is certainly no different. You’ll see everything from close ups of insects to people juggling fire. Simply put,...
In driver 334.89 NVIDIA introduced a new proprietary rendering mode for 3D Vision that enables us to improve the 3D experience for many key DirectX 10 and 11 games. This mode is now called “3D Compatibility Mode”. We have continued to iterate on this feature in driver 344.11, increasing game support and adding some new interface elements. You can get the new driver at www.geforce.com/drivers or via the update option in Geforce Experience. With the release of 344.11, new 3D...
We’re fortunate enough to have another fine 3D video from New Media Film Festival to share with you here on 3DVisionLive—a pop music video from Italy called “The Way,” which you can view here. Even better, New Media Film Festival has provided an interview with one of the co-directors of the video, Edoardo Ballanti, which provides insights on how the video was created and the vision behind it. Enjoy! (Alice Corsi also co-directed the video.) What was the Inspiration behind “...
The Fall Photo Contest received nearly 100 images – thanks to all that entered! The contest called for your best “nature” shots with the only other requirement being that they had to be true stereo images. Submissions ranged from shots of spiders in gardens to artistic approaches to tasteful nudes. As before, members were invited to vote for the winner by tagging images in the contest gallery as favorites. Without further ado, the winner is: Autumn Goodbye to Summer This...
In driver 334.89 NVIDIA introduced a new proprietary rendering mode for 3D Vision that enables us to improve the 3D experience for many key DirectX 10 and 11 games. This mode is now called “3D Compatibility Mode”. We have continued to iterate on this feature in beta driver 337, increasing game support and adding a toggle key to enable/disable the mode. Games with 3D Compatibility Mode will launch in this mode by default. To change the render mode back to standard 3D Vision...

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If there’s ever a time you want to spend less time under the knife, it’s during brain surgery.

Artificial intelligence could help doctors diagnose brain tumors more quickly and more accurately, according to a new study by researchers at the University of Michigan Medical School and Harvard University.

“Our goal is to develop an algorithm that approaches the performance of a neuropathologist at diagnosis during an operation,” said Dr. Daniel Orringer, first author of the study in Nature Biomedical Engineering and an assistant professor of neurosurgery at Michigan Medicine.

Shorter, Safer Surgery

In their experiments on more than 100 brain tissue samples, the researchers used deep learning to detect the presence of a tumor and classify it into one of several broad categories.

The algorithm analyzes tissue from a laser imaging technique the researchers developed called stimulated Raman histology, or SRH. Currently, doctors must halt surgery for 30-40 minutes while tissue sent to the lab is processed, frozen and stained. SRH reduces the wait time to three minutes by making it possible for pathologists to diagnose tumors without the tissue leaving the operating room.

“Helping patients get diagnosed more quickly means patients spend less time in the operating room, which decreases the risks associated with surgery,” said Orringer, a practicing neurosurgeon.

Better Brain Tumor Diagnosis

The deep learning algorithm identified four categories of tumors in the samples. As researchers collect more samples, Orringer said he wants to expand that to eight categories, which would include most of the tumors neurosurgeons encounter.

Accuracy rates on 30 tissue samples tested were 90 percent, compared with neuropathologists’ accuracy rates of 90-95 percent in clinical practice, said Orringer.

“We want to bring accuracy rates up so fewer patients are misdiagnosed,” he said. By enabling prompt, consistent and accurate tissue diagnosis during surgery, deep learning could help fix the problem of variability among pathologists’ diagnoses, Orringer said.

Deep learning would not replace pathologists, whose expertise is needed to make the final diagnosis, he added.

To Operate or Not to Operate

Orringer and his team have tested the technique on more than 370 patients and are driving toward 500.

“The more we feed the computer, the more accurate its diagnoses will become,” Orringer said. The research could be applied to tumors beyond the brain, he added.

An SRH image of brain tumor tissue. The cells with dark, large nuclei in clusters or clumps are tumor cells.

Deep learning and the SRH imaging technique could help doctors make better decisions about how and whether to operate. Some tumors respond better to chemotherapy and radiation than surgery, Orringer said, so patients could avoid surgery entirely.

Neurologists Without Borders

SRH and deep learning could help small hospitals or those in remote areas without access to neurologists, according to the study. Although 1,400 U.S. hospitals perform brain-tumor surgery, there are only 800 board-certified neuropathologists in the country.

Bringing these technologies to smaller hospitals would extend their capabilities because images can be interpreted remotely, Orringer said.

The researchers trained their neural network using the CUDA parallel computing platform, an NVIDIA GeForce GTX 1080 GPU with cuDNN on the Theano deep learning framework.

“GPUs were a vital part of our tool chest for building this algorithm,” Orringer said.

The next step is a large-scale clinical trial, Orringer said. The prototype SRH system and deep learning algorithms are intended for research only.

All images in this story are courtesy of the University of Michigan School of Medicine.

To learn more about how AI computing is changing industries, subscribe to NVIDIA’s AI Podcast on iTunes http://nvda.ws/2hQ4Leb or Google Play Music http://nvda.ws/2hQaIrh.

The post Brain Trust: How AI Is Helping Surgeons Improve Tumor Diagnosis appeared first on The Official NVIDIA Blog.

Tokyo Institute of Technology today announced plans to create Japan’s fastest AI supercomputer, built on NVIDIA’s accelerated computing platform.

The new system, known as TSUBAME3.0, is expected to deliver more than two times the performance of its predecessor, TSUBAME2.5. It will use Pascal-based Tesla P100 GPUs, which are nearly three times as efficient as their predecessors, to reach an expected 12.2 petaflops of double precision performance. That would rank it among the world’s 10 fastest systems according to the latest TOP500 list, released in November.

TSUBAME3.0 will excel in AI computation, expected to deliver more than 47 PFLOPS of AI horsepower. When operated concurrently with TSUBAME2.5, it is expected to deliver 64.3 PFLOPS, making it Japan’s highest performing AI supercomputer.

Excelling in AI computation, TSUBAME3.0 will be Japan’s highest performing AI supercomputer when operated concurrently with its predecessor. TSUBAME3.0

Once up and running this summer, TSUBAME3.0 is expected to be used for education and high-technology research at Tokyo Tech, and be accessible to outside researchers in the private sector. It will also serve as an information infrastructure center for leading Japanese universities.

Tokyo Tech’s Satoshi Matsuoka, a professor of computer science who is building the system, said, “NVIDIA’s broad AI ecosystem, including thousands of deep learning and inference applications, will enable Tokyo Tech to begin training TSUBAME3.0 immediately to help us more quickly solve some of the world’s once unsolvable problems.”

“Artificial intelligence is rapidly becoming a key application for supercomputing,” said Ian Buck, vice president and general manager of Accelerated Computing at NVIDIA. “NVIDIA’s GPU computing platform merges AI with HPC, accelerating computation so that scientists and researchers can drive life-changing advances in such fields as healthcare, energy and transportation.”

The post Tokyo Tech to Build Japan’s Fastest AI Supercomputer Using NVIDIA’s Accelerated Computing Platform appeared first on The Official NVIDIA Blog.