Skip to main content

3D News

In our opinion, there are far too few people out there taking 3D images and one major reason is the perceived difficulty barrier—taking two images and combining them for a stereo effect with special software or using custom twin digital SLR camera rigs is simply too complex and/or expensive for most of us mere mortals. Enter the 3D-capable point-and-shoot, the latest of which is Panasonic’s upcoming Lumix DMC-3D1. Similar to Fujifilm...

Recent Blog Entries

For businesses that run on Cisco blade and rack servers, and want to extend their reach with desktop virtualization using the latest high-performance graphics, the wait is over.

NVIDIA GRID 2.0 with the NVIDIA Tesla M60 GPU accelerator is now supported on the Cisco UCS C240 M4 rack server. And, in a first for GRID-based Cisco blades, Cisco is supporting NVIDIA GRID 2.0 with the NVIDIA Tesla M6 on its UCS B200 M4 blade server.

These combinations bring unparalleled graphics performance to high-end applications on any device, anywhere. Organizations can expand their virtualization footprint without compromising performance, user experience or security. They can boost employee productivity with faster access to files and real-time collaboration. And they can centralize IT, so all workloads can be managed and delivered from the data center.

Cisco’s Workhorse of Virtualization Deployments Virtualization workhorse: Cisco UCS B200 M4 blade servers.

The Cisco UCS B200 M4 blade server is one of the newest in the Cisco UCS portfolio. It delivers enterprise-class performance, flexibility and optimization for data centers and remote sites. It also offers excellent virtual desktop density per blade and ease of management, making for an attractive total cost of ownership.

Key to the Cisco blade’s popularity is its predictable and scalable performance for virtual desktop users. Cisco and NVIDIA have created an unmatched level of integration between the UCS B200 M4 and Tesla M6 card. The card can be discovered and managed via Cisco UCS Manager for administration through a single pane of glass

Working on the Edge, with NVIDIA GRID

NVIDIA GRID on Cisco blade servers opens new opportunities in industries such as manufacturing, oil and gas, and design, where businesses using applications from the likes of ESRI, AutoCAD, Petrel or Siemens want the flexibility to work from anywhere but require the same high-end application performance on a virtual desktop as they do on a physical one.

By bringing graphics acceleration to virtualization, NVIDIA GRID unlocks the promise of productivity, mobility, security and flexibility for every user. NVIDIA GRID technology on Cisco blade servers pushes desktop virtualization to the edge.

For more information or to request a demo go here.

The post Blade Runner: NVIDIA GRID 2.0 Now Available on Cisco Blade and Rack Servers appeared first on The Official NVIDIA Blog.

“All clear.” That’s the terrific news Jeet Samarth Raut’s mother heard after a radiological scan.

Two weeks later, a second opinion revealed breast cancer. Certain that technology can do better, the young entrepreneur uses deep learning software powered by NVIDIA GPUs to reduce the number of incorrect diagnoses.

Whether in Raut’s rural Illinois hometown (where his mother began treatment and recovered) or in developing countries, failures in scanning, perception and interpretation can hamper accurate diagnoses.

Raut and fellow entrepreneur and Columbia University alum Peter Wakahiu Njenga co-founded, a startup based in New York that’s trying to make it easier for healthcare practitioners to accurately identify diseases from ordinary radiology image data. founders Peter Wakahiu Njenga and Jeet Samarth Raut

“A radiologist is looking for patterns on a scan, but it’s done manually and there’s such room for error,” said Raut, who spent several years working as a research assistant at Stanford University’s Phonetics Lab, Life-span Development Lab, and Computers and Cognition Lab.

“Computer vision has gotten really good at this, so we’re using technology to make these diagnoses more accurate by drawing from bigger datasets,” he said.’s goal is to build a portable system with gear that can be used in developing regions, low-income areas and zones where healthcare is scattered.

A Personal Mission’s software, combined with neural network architectures and NVIDIA GPUs, trains computers to identify and process thousands of existing medical images. These are tagged as healthy or diseased, with the system absorbing feedback from radiologists to improve tagging accuracy as it encounters more examples.’s software tags images showing diabetic retinopahty, one of the leading causes of blindness in the world.

Healthcare practitioners in the field would no longer have to rely solely on their training and individual experience to spot diseased tissues. When interpreting visual data from MRIs, CT scans and retinal images, they’d get an assist from the large-scale visual recognition work performed by computers. This allows health assessments by technicians to be carried out anywhere from refugee camps to remote villages.

“Our technology is applicable in developing and low-income areas,” said Njenga, who previously worked at Facebook on machine learning. “You can take an image of an affected area, compare it with a database of similar images, get a diagnosis and start treatment.”

Deep Learning Makes It Work

After a patient has a scan taken at a medical imaging center, it’s sent for review to a radiologist and also’s servers.’s deep learning technology analyzes the scans to detect anomalies. The scans are returned to the radiologist with tags listing the ailments generated by’s models, which have been trained on giant datasets.

Having the medical images read by two parties reduces the chances of false positives or false negatives, Raut said.

To identify abnormalities in medical images, uses a class of artificial neural networks called convolutional neural networks, or ConvNets. Inspired by the lattice-like visual cortex of the brain, ConvNets are specialized for image processing tasks and use pattern recognition to perform object classification.

“We harness the massive parallelism of high-performance NVIDIA GPUs to speed up the process,” Njenga said. Advances in GPU programming enabled to have a deep ConvNet with 50 million parameters. The underlying algorithms use cuDNN, NVIDIA’s library of GPU-accelerated software building blocks for deep neural networks, Njenga said. is looking to pilot its technology next year with an established healthcare provider.

The post Startup Uses Deep Learning to Detect Disease from Medical Scans appeared first on The Official NVIDIA Blog.