Deep Learning Paves Way for Better Diagnostics

By Tiffany Trader

September 19, 2016

Stanford researchers are leveraging GPU-based machines in the Amazon EC2 cloud to run deep learning workloads with the goal of improving diagnostics for a chronic eye disease, called diabetic retinopathy. The disease is a complication of diabetes that can lead to blindness if blood sugar is poorly controlled. It affects about 45 percent of diabetics and 100 million people worldwide, many in developing nations.

Final-year Stanford PhD students Apaar Sadhwani and Jason Su got involved in developing the diagnostic solution as part of a class project and corresponding Kaggle competition that was held last year. Sponsor Amazon provided AWS cloud credits in support of the research.

diabetic-retinopathy_5-classes_sadhwani-su_400x
Source: Automatic Grading of Eye Diseases Through Deep Learning, 2016

After Kaggle, the duo decided to turn their research project into a cloud-based platform that hospitals and clinics can use to guide the diagnosis of eye diseases. Their approach relies on a convolutional neural net (CNN) that grades the severity of diabetic retinopathy disease states into five categories: 0-4, with 0 being normal and 4 being the most severe.

The researchers have been training their model with a data set of 80,000 images from EYEPACS, a web-based application for exchanging eye-related clinical information, run by the California Health Foundation. “Getting data is the most constraining part of applying deep learning to a medical setting,” said Sadhwani, “but we are working closely with partners to get more data.”

They’ve also had to address a class imbalance in the data set. “We have a lot more 0’s and 1’s than 3’s and 4’s, for example,” said Sadhwani. As the disease progresses to stage four (known as proliferative diabetic retinopathy, or PDR), image data is more rare. A total of about 10,000 stage four images are required for optimal results.

The training problem is run on AWS Elastic Compute Cloud (EC2) with single-GPU and multi-GPU nodes. Some S3 storage and Elastic Block Store (EBS) services are also employed. The training takes about three days to a week for a given model.

Within EC2, the researchers are using Starcluster which lets them build custom clusters among the nodes and network them together. They used a master node to store all their training data and up to 28 different training nodes. All these separate training nodes would access the master node so they wouldn’t have to mirror the data onto each of the nodes.

“With Starcluster and AWS you can bring up different node types independently on demand,” said Su. “So we would run this experiment that would only need a single-GPU node and then after that finished we could shut down that node and save money. Then we would scale it up to a larger resolution image and we would need four-GPU nodes for that – so we’d spin that up, train on that, and come back three days later and shut that off. AWS provides this flexibility for scaling up and scaling down for cost and for trying out different ideas.”

The researchers relied on AWS spot instance pricing to further improve the economics. Their program saves a state every “epoch,” which relates to one pass through the data set, so losing a node did not incur a big setback. With 55 epochs in a run, the most they would lose is 1/55th of their training progress.

They used the g2.2xlarge instance type and the g2.8xlarge instance type for training their final models. They trained two kinds of models, one on low-res images and the final model on high-res images, for which they employed the larger multi-GPU nodes.

Amazon’s GPU instances are based on older Nvidia GRID K520 graphics cards, which at 4 GB per GPU do not have an ideal memory profile for training based on very high-resolution images.

“Typically in deep learning, you have a 256×256 image, or about one-sixteenth of a megapixel and we’re at four megapixels, so memory is a huge part of doing this problem,” said Sadhwani. “Our workaround was to scale to 4-GPU nodes, which effectively had 4 gigabytes of memory each [GPU], but we lose some to overhead because we have to have the model independently at each of the separate GPUs. It would be more advantageous to have a single GPU with a full 16 gigabytes.”

Because their model was dealing with these high-resolution images, they used Torch to split it across the 4-GPU node to fine-tune its parameters. Currently, they are moving to a distributed training model, which enables several different nodes to train essentially the same model but with independent data. This gives them the ability to train one model across many GPUs, rather than a single model on a single GPU node and thus accelerates the training.

The researchers are eyeing clouds with higher-memory GPUs, which could mean holding out for upgraded Amazon instances or moving to the Microsoft Azure cloud with its Tesla K80s.

They are not interested in CPUs. “It would take significantly longer, at least a factor of 50,” said Sadhwani. “The kind of neural networks we are using [convolutional neural nets] harness parallelization a lot. Even if we were not using this special class of network, there is at least a 10x speedup going from CPUs to GPUs, but for this particular variety that speedup is magnified a lot more, in the neighborhood of 100x.”

Diabetic retinopathy is a disease of the blood vessels in the eye. As the sugar level in the blood rises, it causes the walls of the blood vessels to thin and eventually they’ll crack and bleed. The most important thing to look for is tiny dot bleeds, called hemorrhages. They are very small and difficult to locate even with advanced algorithms. The deep learning model must also be trained to ignore or flag likely camera artifacts, which appear in approximately 40 percent of the images, and can obscure identification of disease traits.

To address these challenges, the Stanford team’s approach uses two networks, a lesion detector and a main network. The lesion detector looks at a small part of the image and outputs a number between 0 and 1, a probability. The lesion detector has so far achieved an accuracy of 99 percent for negatives and 76 percent for positives. The purpose of the main network is to characterize details about where the disease-related features are with respect to the important parts of the eye.

deep-learning-fused-architecture_sadhwani-su_800x
Source: Automatic Grading of Eye Diseases Through Deep Learning, 2016

The outputs of these two pipelines are then fused together. This provides a way to combine low-level details about where there are dot hemorrhages with high-level information like which parts of the image should actually be ignored because they are corrupted by artifacts. The fuse network is responsible for integrating all these signals together to deliver a final probability for the disease class.

Right now the team has been working with five classes, but they say that in the clinical setting, these grades are not tracked with such granularity. In terms of intervention, there are really three stages: 0) no action is required; 1) monitor the progress of the disease; and 2) medical intervention such as surgery is required.

“Moving to three-classes would increase the accuracy of our models because it’s a simpler problem and easier to solve,” said Su.

The ultimate goal here is to deliver a digital assistant to radiologists, opthamologists and other clinicians, so they can screen more patients, more frequently.

“Using an automated tool to augment human resources, you can more closely monitor the changes in the disease state as they progress to more effectively treat the disease,” said Su.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore (~$675 million) supercomputing project, approved by the Ind Read more…

By Tiffany Trader

Tuning InfiniBand Interconnects Using Congestion Control

July 26, 2017

InfiniBand is among the most common and well-known cluster interconnect technologies. However, the complexities of an InfiniBand (IB) network can frustrate the most experienced cluster administrators. Maintaining a balan Read more…

By Adam Dorsey

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a community infrastructure in support of machine learning research Read more…

By John Russell

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

DARPA Continues Investment in Post-Moore’s Technologies

July 24, 2017

The U.S. military long ago ceded dominance in electronics innovation to Silicon Valley, the DoD-backed powerhouse that has driven microelectronic generation for decades. With Moore's Law clearly running out of steam, the Read more…

By George Leopold

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore Read more…

By Tiffany Trader

Tuning InfiniBand Interconnects Using Congestion Control

July 26, 2017

InfiniBand is among the most common and well-known cluster interconnect technologies. However, the complexities of an InfiniBand (IB) network can frustrate the Read more…

By Adam Dorsey

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provid Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This