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Dear 3D Vision Fans, We have enjoyed sharing 3D Vision content here over the past eight years, and seeing the fantastic images and videos you’ve uploaded. Thanks for making this such a fun destination for 3D enthusiasts. Usage of this site has been declining, and therefore, we are going to transition the 3D photo function of this site over to The last day that you will be able to access photos is Aug. 1, 2018. In addition, the 3D video...

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Ever since a Dutch cloth merchant accidentally discovered bacteria in 1676, microscopes have been a critical tool for medicine. Today’s microscopes are 800,000 times more powerful than the human eye, but they still need a person to scrutinize what’s under the lens.

That person is usually a pathologist — and that’s a problem. Worldwide, there are too few of these doctors who interpret lab tests to diagnose, monitor and treat disease.

Now SigTuple, a member of our Inception startup incubator program, is testing an AI microscope that could help address the pathologist shortage. The GPU-powered device automatically scans and analyzes blood smears and other biological samples to detect problems.

Global workforce capacity in pathology and laboratory medicine. Image reprinted from The Lancet, Access to pathology and laboratory medicine services: a crucial gap. Copyright (2018), with permission from Elsevier. One in a Million

The dearth of pathologists is crucial problem in the poorest countries, where patients lacking a proper diagnosis are often given inappropriate treatments, according to studies published this month in The Lancet medical journal. In sub-Saharan Africa, for example, there is a single pathologist for every million people, the journal reported.

But the problem isn’t confined to poor countries. In China, there’s one pathologist for every 130,000 people, The Lancet reported. That compares with 5.7 per 100,000 people in the U.S., according to the most recent figures available. And in the U.S., studies predict the number of pathologists will shrink to 3.7 per 100,000 people by 2030.

In India, that’s now 1 pathologist per 65,000 people — a total of 20,000 pathologists available to treat the nation of 1.3 billion people, said Tathagato Rai Dastidar, co-founder and chief technology officer of Bangalore-based SigTuple.

“There is a human cost here. In many places, where there is no pathologist, a half-trained technician will write out a report and cases will go undetected until it’s too late,” Dastidar said.

SigTuple’s automated microscope costs a fraction of what existing devices do, making it affordable for developing countries where pathologists are few. Image courtesy of SigTuple. Low-Cost, High-Performance Microscope

SigTuple’s device isn’t the first automated microscope. Instruments known as digital slide scanners automatically convert glass slides to digital images and interpret the results. But SigTuple’s microscope sells for a fraction of the price of digital slide scanners, making it affordable for most labs, including those in the developing world.

The company’s AI microscope works by scanning slides under its lens and then using GPU-accelerated deep learning to analyze the digital images either on SigTuple’s AI platform in the cloud or on the microscope itself. It uses different deep learning models to analyze blood, urine and semen.

The microscope performs functions like identifying cells, classifying them into categories and subcategories, and calculating the numbers of different cell types.

For a blood smear, for example, Shonit — that’s Sanskrit for blood — identifies red and white blood cells and platelets, pinpoints their locations and calculates ratios of different types of white blood cells (commonly known as differential count). It also computes 3D information about cells from their 2D images using machine learning techniques.

In studies SigTuple conducted with some of India’s leading labs, Shonit’s accuracy matched that of other automated analyzers. It also successfully identified rare varieties of cells that both pathologists and automated tools usually miss.

Expert Review in the Cloud

In addition to providing a low-cost method for interpreting slides, Dastidar sees SigTuple’s AI platform as an ideal tool for providing expert review of tests when no expert is available. As well as automating analysis, it stores data in the cloud so any pathologist anywhere can interpret test results.

The company’s cloud platform also makes it far easier for pathologists to collaborate on difficult cases.

“Before that would have meant shipping the slide from one lab to another,” Dastidar said.

SigTuple next plans a formal trial of Shonit and is beginning to roll it out commercially.

For more information about SigTuple and Shonit, watch Dastidar’s GTC talk or read SigTuple’s recent paper, Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning.

The post Path Math: How AI Can Find a Way Around Pathologist Shortage appeared first on The Official NVIDIA Blog.

Automotive safety isn’t a box you check. It’s not a feature. Safety is the whole point of autonomous vehicles. And it starts with a new class of computer, a new type of software and a new breed of chips.

Safety is designed into the NVIDIA DRIVE computer for autonomous vehicles from the ground up. Experts architect safety technology into every aspect of our computing system, from the hardware to the software stack. Tools and methods are developed to create software that performs as intended, reliably and with backups. Stringent engineering processes are developed to ensure no corners are cut.

“Safety-first” computer design is equal parts expertise, architecture, design, tools, methods and best practices. Safety needs to be everywhere — permeating our engineering culture.

Top Experts Agree – Xavier Is Architected for Safety

We didn’t stop there. We invited the world’s top automotive safety and reliability company, TÜV SÜD, to perform a safety concept assessment of our new NVIDIA Xavier system-on-chip (SoC). The 150-year-old German firm’s 24,000 employees assess compliance to national and international standards for safety, durability and quality in cars, as well as for factories, buildings, bridges and other infrastructure.

“NVIDIA Xavier is one of the most complex processors we have evaluated,” said Axel Köhnen, Xavier lead assessor at TÜV SÜD RAIL. “Our in-depth technical assessment confirms the Xavier SoC architecture is suitable for use in autonomous driving applications and highlights NVIDIA’s commitment to enable safe autonomous driving.”

Feeds and Speeds Built Around a Single Need: Safety

Let’s walk through what that means.

As the world’s first autonomous driving processor, Xavier is the most complex SoC ever created. Its 9 billion transistors enable Xavier to process vast amounts of data. Its GMSL (gigabit multimedia serial link) high-speed IO connects Xavier to the largest array of lidar, radar and camera sensors of any chip ever built.

Inside the SoC, six types of processors — ISP (image signal processor), VPU (video processing unit), PVA (programmable vision accelerator), DLA (deep learning accelerator), CUDA GPU, and CPU — process nearly 40 trillion operations per second, 30 trillion for deep learning alone. This level of processing is 10x more powerful than our previous generation DRIVE PX 2 reference design, which is used in today’s most advanced production cars.

These aren’t feeds and speeds we enabled just because we could. They’re essential to safety.

1 Chip, 6 Processors, 40 TOPS – Diversity and Redundancy Need Performance

Xavier is the brain of the self-driving car. From a safety perspective, this means building in diversity, redundancy and fault detection from end to end. From sensors, to specialized processors, to algorithms, to the computer, all the way to the car’s actuation — each function is performed using multiple methods, which gives us diversity. And each vital function has a fallback system, which ensures redundancy.

For example, objects detected by radar, lidar or cameras are handled with different processors and perceived using a variety of computer vision, signal processing and point cloud algorithms. Multiple deep learning networks run concurrently to recognize objects that should be avoided, while other networks determine where it’s safe to drive, achieving both diversity and redundancy. Different processors, running diverse algorithms in parallel, backing each other up, reduce the chance of an undetected single point of failure.

Inside the Xavier SoC — six types of processors, processing nearly 40 trillion operations per second.

Xavier also includes many types of hardware diagnostics. Key areas of logic are duplicated and voted in hardware using lockstep comparators. Error-correcting codes on memories detect faults and improve availability. A unique built-in self-test helps to find faults in the diagnostics, wherever they may be on chip.

Xavier’s safety architecture was created over several years by more than 300 architects, designers and safety experts who analyzed over 150 safety-related modules. With Xavier, the auto industry can achieve the highest functional safety rating: ASIL-D.

Building for diversity and redundancy needed for safety demands a huge amount of extra processing. For self-driving cars, processing power translates to safety.

Measuring Up to the Highest Standards

Thousands of engineers writing millions of lines of code — how do we ensure Xavier does what we designed it to do?

We created DRIVE as an open platform so that the experts in the world’s best car companies can engage our platform to make it industrial strength. We also turned to TÜV SÜD, among the world’s most respected safety experts, who measured Xavier against the automotive industry’s standard for functional safety — ISO 26262.

Established by the International Organization for Standardization, the world’s chief standards body, ISO 26262 is the definitive global standard for the functional safety — a system’s ability to avoid, identify and manage failures — of road vehicles’ systems, hardware and software.

To meet that standard, an SoC must have an architecture that doesn’t just detect hardware failures during operation. It also needs to be developed in a process that mitigates potential systematic faults. That is, the SoC must avoid failures whenever possible, but detect and respond to them if they cannot be avoided.

TÜV SÜD’s team determined Xavier’s architecture meets the ISO 26262 requirements to avoid unreasonable risk in situations that could result in serious injury.

Our Journey to Zero Accidents

Inventing technology that will one day eliminate accidents on our roads is one of NVIDIA’s most important endeavors. We are inspired to tackle this grand computing challenge that will have great social impact.

We had to re-invent every aspect of computing, starting with the Xavier processor. We created processing power not for speed, but for safety. We benchmarked ourselves against the highest standards: ASIL-D and ISO 26262. And we engaged every expert — from the best car companies to TÜV SÜD — to test and challenge us.

The journey is long, but the destination is worth every step.

The post DRIVE Xavier, World’s First Single-Chip Self-Driving Car Processor, Gets Approval from Top Safety Experts appeared first on The Official NVIDIA Blog.