Deep Learning AI on XSEDE Systems Promises Fewer False Alarms and Early Prediction of Breast Cancer

September 9, 2019

September 9, 2019 — Screening mammography is an important tool against breast cancer. But it has its limitations. A “normal” screening mammogram means that a woman doesn’t have cancer now. But doctors wonder whether “normal” images contain clues about a woman’s risk of developing breast cancer in the future. Also, most women “recalled” for more tests when their mammograms show suspicious masses don’t have cancer. With the help of XSEDE’s Extended Collaborative Support Service and Novel and Innovative Projects, scientists at the University of Pittsburgh Medical Center are using the XSEDE-allocated Bridges-AI supercomputer at the Pittsburgh Supercomputing Center to run artificial intelligence programs meant to determine the risk of developing breast cancer and to prevent false recalls.

Using AIs to identify false recalls by classifying the three categories (negative, false recalls, and malignancy) of digital mammogram images in breast cancer screening. Image courtesy of XSEDE

Why It’s Important

Despite a lot of progress in improving survival and quality of life for women with breast cancer, the disease remains a major threat to women’s health. It’s the most common cancer in women and is either the first or second most common cause of cancer death for women in the largest racial and ethnic groups, accounting for 41,000 deaths in 2016 alone.

Screening mammography is an important tool for getting early warning, when the disease is easiest to treat. But it’s not perfect. For women whose scans show no signs of breast cancer, doctors wonder whether that scan may contain information they could use to predict future risk. More than 10 percent of women who get mammograms are “recalled” for further testing. But nearly 90 percent of the time it’s a false alarm. That’s something like 3 million women in the U.S. who go through the stress of unnecessary recall each year.

“We collected a large set of images from UPMC’s digital screening mammography … We wanted to see if ‘normal,’ or negative, digital mammography images in screening were predictive for the risk of breast cancer in the future. There’s also the very critical breast-cancer screening issue that when a lot of women come for mammography and when radiologists visually assess their images, it’s not certain in many images whether cancer is present or not. This creates a difficult decision-making process on whether to ask these women to come back for additional workup,” said Shandong Wu, University of Pittsburgh Medical Center

Expert radiologists can tell a lot from a modern digital mammogram. But Shandong Wu and his colleagues at the University of Pittsburgh Medical Center (UPMC) wondered if artificial intelligence (AI) could detect subtle hints in mammograms that the human eye can’t see. They tested their “deep learning” AIs on digital mammograms from UPMC patients whose status was already known, running the programs on the XSEDE-allocated Bridges and Bridges-AI systems at the Pittsburgh Supercomputing Center.

How XSEDE Helped

The task for the UPMC scientists’ deep-learning software was a big one. Each digital mammogram is more than 2,000 by 3,000 pixels large—that’s dozens of megabytes of data for each image. And to do their study, they needed to “train” and then test their AIs on thousands of images. Their AIs are also pretrained on large datasets with tens of thousands of images. The size of the data for AI modeling was enormous, making the computations slow on the computers available to the researchers at their own laboratory.

“[Roberto] was really helpful. He converted a Matlab container to Singularity, and he wrote a wrapper to run Matlab R2019a on the DGX-2. Sergiu Sanielevici [leader of XSEDE’s Novel and Innovative Projects] has been very helpful in supporting our research and he regularly inquires our progress and needs, making sure our issues are properly addressed. Tom Maiden, Rick Costa and Bryan Learn also assisted us in solving problems. Without the support of XSEDE, I don’t think we would have been able to do this work.”—Shandong Wu, University of Pittsburgh Medical Center

Working with experts from XSEDE’s Novel and Innovative Projects program and Extended Collaborative Support Service (ECSS), including Roberto Gomez and other ECSS staff at PSC, the team used the graphics processing unit (GPU) nodes of Bridges-AI to train and run their AIs. Deep learning, which works by building up layers of different kinds of information and then pruning connections between the layers that don’t produce the desired result, tends to work best on GPUs. The new NVIDIA “Volta” GPUs in Bridges-AI contain accelerators, called “tensor cores,” specifically designed for deep learning. Bridges-AI’s GPU nodes combine eight to 16 GPUs each for up to 512 gigabytes of extremely fast GPU memory. The large memory available to PSC’s GPU nodes was central to the success of the AIs, bringing the computation time down from weeks to hours. The NVIDIA DGX-2 node, deployed in Bridges-AI as a first for open research, and its massive memory were particularly useful.

When an AI is designed to produce a binary result—yes or no, positive or negative—scientists often report that result as a graph of true positives versus false positives. The larger the “area under the curve,” or AUC, the better the AI’s accuracy. AUC can range from zero to one, where zero means that the classifier has no predictive value and one implies perfect prediction. Versions of the screening AIs had an AUC of 0.73 when predicting whether a woman with a negative mammogram would develop cancer over the next 1.5 years. Better, the recall AIs could tell the difference between women with cancer and those who would have been recalled even though they didn’t have cancer with an AUC of 0.80. These results are promising, and could have value for clinical use after further evaluation.

“In this kind of work—medical images—we deal with larger-scale and sometime 3D volumetric data. All those images are high-resolution images … and we have more than 10 thousand [of them]. Our local GPUs did not have enough memory to accommodate such a scale of data for AI modeling. It could take weeks to run one experiment without the support of powerful GPUs. Using the GPUs from XSEDE, with larger memory, reduced that to a couple of hours,” said Shandong Wu, University of Pittsburgh Medical Center.

With XSEDE support, Dr. Wu’s lab is developing several other AIs to improve breast cancer diagnosis. One would pre-read digital breast tomosynthesis—a kind of 3D breast imaging method—to reduce the time it takes radiologists to read the scans. Another is designed to automatically identify and correct mistakes in the labeling in a dataset for AI learning. Finally, the scientists are also working on AIs to predict breast cancer pathology test markers and the recurrence risk for women who’ve already been diagnosed with breast cancer.

Further work beyond these preliminary results will compare the benefits of improved AIs against the current methods used by doctors. The aim is to improve care and lower cost in real-world clinical practice. The UPMC team reported their results in five oral presentations at the Radiological Society of North America (RSNA) Annual Meeting in Chicago last year, three presentations at the Society of Photo-Optical Instrumentation Engineers Medical Imaging conference in San Diego this year, and several upcoming journal manuscripts.


Source: The Extreme Science and Engineering Discovery Environment (XSEDE)

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!

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen 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…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. 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…

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…

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…

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…

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…

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…

Leading Solution Providers

Contributors

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…

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…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer 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…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire