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 industry updates delivered to you every week!

Empowering High-Performance Computing for Artificial Intelligence

April 19, 2024

Artificial intelligence (AI) presents some of the most challenging demands in information technology, especially concerning computing power and data movement. As a result of these challenges, high-performance computing Read more…

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that have occurred about once a decade. With this in mind, the ISC Read more…

2024 Winter Classic: Texas Two Step

April 18, 2024

Texas Tech University. Their middle name is ‘tech’, so it’s no surprise that they’ve been fielding not one, but two teams in the last three Winter Classic cluster competitions. Their teams, dubbed Matador and Red Read more…

2024 Winter Classic: The Return of Team Fayetteville

April 18, 2024

Hailing from Fayetteville, NC, Fayetteville State University stayed under the radar in their first Winter Classic competition in 2022. Solid students for sure, but not a lot of HPC experience. All good. They didn’t Read more…

Software Specialist Horizon Quantum to Build First-of-a-Kind Hardware Testbed

April 18, 2024

Horizon Quantum Computing, a Singapore-based quantum software start-up, announced today it would build its own testbed of quantum computers, starting with use of Rigetti’s Novera 9-qubit QPU. The approach by a quantum Read more…

2024 Winter Classic: Meet Team Morehouse

April 17, 2024

Morehouse College? The university is well-known for their long list of illustrious graduates, the rigor of their academics, and the quality of the instruction. They were one of the first schools to sign up for the Winter Read more…

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that ha Read more…

Software Specialist Horizon Quantum to Build First-of-a-Kind Hardware Testbed

April 18, 2024

Horizon Quantum Computing, a Singapore-based quantum software start-up, announced today it would build its own testbed of quantum computers, starting with use o Read more…

MLCommons Launches New AI Safety Benchmark Initiative

April 16, 2024

MLCommons, organizer of the popular MLPerf benchmarking exercises (training and inference), is starting a new effort to benchmark AI Safety, one of the most pre Read more…

Exciting Updates From Stanford HAI’s Seventh Annual AI Index Report

April 15, 2024

As the AI revolution marches on, it is vital to continually reassess how this technology is reshaping our world. To that end, researchers at Stanford’s Instit Read more…

Intel’s Vision Advantage: Chips Are Available Off-the-Shelf

April 11, 2024

The chip market is facing a crisis: chip development is now concentrated in the hands of the few. A confluence of events this week reminded us how few chips Read more…

The VC View: Quantonation’s Deep Dive into Funding Quantum Start-ups

April 11, 2024

Yesterday Quantonation — which promotes itself as a one-of-a-kind venture capital (VC) company specializing in quantum science and deep physics  — announce Read more…

Nvidia’s GTC Is the New Intel IDF

April 9, 2024

After many years, Nvidia's GPU Technology Conference (GTC) was back in person and has become the conference for those who care about semiconductors and AI. I Read more…

Google Announces Homegrown ARM-based CPUs 

April 9, 2024

Google sprang a surprise at the ongoing Google Next Cloud conference by introducing its own ARM-based CPU called Axion, which will be offered to customers in it Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Leading Solution Providers

Contributors

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Eyes on the Quantum Prize – D-Wave Says its Time is Now

January 30, 2024

Early quantum computing pioneer D-Wave again asserted – that at least for D-Wave – the commercial quantum era has begun. Speaking at its first in-person Ana Read more…

GenAI Having Major Impact on Data Culture, Survey Says

February 21, 2024

While 2023 was the year of GenAI, the adoption rates for GenAI did not match expectations. Most organizations are continuing to invest in GenAI but are yet to Read more…

The GenAI Datacenter Squeeze Is Here

February 1, 2024

The immediate effect of the GenAI GPU Squeeze was to reduce availability, either direct purchase or cloud access, increase cost, and push demand through the roof. A secondary issue has been developing over the last several years. Even though your organization secured several racks... Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire