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 industy updates delivered to you every week!

Dell’s AMD-Powered Server Line Targets High-End Jobs

September 17, 2019

Dell Technologies rolled out five new servers this week based on AMD’s latest Epyc processor that are geared toward data-driven workloads running on increasingly popular multi-cloud platforms as well as in the HPC data Read more…

By George Leopold

Cerebras to Supply DOE with Wafer-Scale AI Supercomputing Technology

September 17, 2019

Cerebras Systems, which debuted its wafer-scale AI silicon at Hot Chips last month, has entered into a multi-year partnership with Argonne National Laboratory and Lawrence Livermore National Laboratory as part of a larger collaboration with the U.S. Department of Energy... Read more…

By Tiffany Trader

Better Scientific Software: Turn Your Passion into Cash

September 13, 2019

Do you know your way around scientific software and programming? You think you can contribute to the community by making scientific software better? If so, then the Better Scientific Software (BSSW) organization wants yo Read more…

By Dan Olds

AWS Solution Channel

A Guide to Discovering the Best AWS Instances and Configurations for Your HPC Workload

The flexibility and heterogeneity of HPC cloud services provide a welcome contrast to the constraints of on-premises HPC. Every HPC configuration is potentially accessible to any given workload in a well-resourced cloud HPC deployment, with vast scalability to spin up as much compute as that workload demands in any given moment. Read more…

HPE Extreme Performance Solutions

Intel FPGAs: More Than Just an Accelerator Card

FPGA (Field Programmable Gate Array) acceleration cards are not new, as they’ve been commercially available since 1984. Typically, the emphasis around FPGAs has centered on the fact that they’re programmable accelerators, and that they can truly offer workload specific hardware acceleration solutions without requiring custom silicon. Read more…

IBM Accelerated Insights

Rumors of My Death Are Still Exaggerated: The Mainframe

[Connect with Spectrum users and learn new skills in the IBM Spectrum LSF User Community.]

As of 2017, 92 of the world’s top 100 banks used mainframes. Read more…

Google’s ML Compiler Initiative Advances

September 12, 2019

Machine learning models running on everything from cloud platforms to mobile phones are posing new challenges for developers faced with growing tool complexity. Google’s TensorFlow team unveiled an open-source machine Read more…

By George Leopold

Cerebras to Supply DOE with Wafer-Scale AI Supercomputing Technology

September 17, 2019

Cerebras Systems, which debuted its wafer-scale AI silicon at Hot Chips last month, has entered into a multi-year partnership with Argonne National Laboratory and Lawrence Livermore National Laboratory as part of a larger collaboration with the U.S. Department of Energy... Read more…

By Tiffany Trader

IDAS: ‘Automagic’ HPC With Training Wheels

September 12, 2019

High-performance computing (HPC) for research is notorious for having steep barriers to entry. For this reason, high-tech disciplines were early adopters, have Read more…

By Elizabeth Leake

Univa Brings Cloud Automation to Slurm Users with Navops Launch 2.0

September 11, 2019

Univa, the company behind Grid Engine, announced today its HPC cloud-automation platform NavOps Launch will support the popular open-source workload scheduler Slurm. With the release of NavOps Launch 2.0, “Slurm users will have access to the same cloud automation capabilities... Read more…

By Tiffany Trader

When Dense Matrix Representations Beat Sparse

September 9, 2019

In our world filled with unintended consequences, it turns out that saving memory space to help deal with GPU limitations, knowing it introduces performance pen Read more…

By James Reinders

Eyes on the Prize: TACC’s Frontera Quickly Ramps up Science Agenda

September 9, 2019

Announced a year ago and officially launched a week ago, the Texas Advanced Computing Center’s Frontera – now the fastest academic supercomputer (~25 petefl Read more…

By John Russell

Quantum Roundup: IBM Goes to School, Delft Tackles Networking, Rigetti Updates

September 5, 2019

IBM today announced a new open source quantum ‘textbook’, a series of quantum education videos, and plans to expand its nascent quantum hackathon program. L Read more…

By John Russell

DARPA Looks to Propel Parallelism

September 4, 2019

As Moore’s law runs out of steam, new programming approaches are being pursued with the goal of greater hardware performance with less coding. The Defense Advanced Projects Research Agency is launching a new programming effort aimed at leveraging the benefits of massive distributed parallelism with less sweat. Read more…

By George Leopold

Fastest Academic Supercomputer Enters Full Production at TACC, Just in Time for Hurricane Season

September 3, 2019

Frontera, the NSF supercomputer installed at the Texas Advanced Computing Center (TACC) in June, passed its formal acceptance last week and is now officially la Read more…

By Tiffany Trader

High Performance (Potato) Chips

May 5, 2006

In this article, we focus on how Procter & Gamble is using high performance computing to create some common, everyday supermarket products. Tom Lange, a 27-year veteran of the company, tells us how P&G models products, processes and production systems for the betterment of consumer package goods. Read more…

By Michael Feldman

Supercomputer-Powered AI Tackles a Key Fusion Energy Challenge

August 7, 2019

Fusion energy is the Holy Grail of the energy world: low-radioactivity, low-waste, zero-carbon, high-output nuclear power that can run on hydrogen or lithium. T Read more…

By Oliver Peckham

AMD Verifies Its Largest 7nm Chip Design in Ten Hours

June 5, 2019

AMD announced last week that its engineers had successfully executed the first physical verification of its largest 7nm chip design – in just ten hours. The AMD Radeon Instinct Vega20 – which boasts 13.2 billion transistors – was tested using a TSMC-certified Calibre nmDRC software platform from Mentor. Read more…

By Oliver Peckham

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... Read more…

By John Russell

DARPA Looks to Propel Parallelism

September 4, 2019

As Moore’s law runs out of steam, new programming approaches are being pursued with the goal of greater hardware performance with less coding. The Defense Advanced Projects Research Agency is launching a new programming effort aimed at leveraging the benefits of massive distributed parallelism with less sweat. Read more…

By George Leopold

Cray Wins NNSA-Livermore ‘El Capitan’ Exascale Contract

August 13, 2019

Cray has won the bid to build the first exascale supercomputer for the National Nuclear Security Administration (NNSA) and Lawrence Livermore National Laborator Read more…

By Tiffany Trader

AMD Launches Epyc Rome, First 7nm CPU

August 8, 2019

From a gala event at the Palace of Fine Arts in San Francisco yesterday (Aug. 7), AMD launched its second-generation Epyc Rome x86 chips, based on its 7nm proce Read more…

By Tiffany Trader

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour

CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
GOOGLE
GOOGLE
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
VERNE GLOBAL
VERNE GLOBAL

Ayar Labs to Demo Photonics Chiplet in FPGA Package at Hot Chips

August 19, 2019

Silicon startup Ayar Labs continues to gain momentum with its DARPA-backed optical chiplet technology that puts advanced electronics and optics on the same chip Read more…

By Tiffany Trader

Top500 Purely Petaflops; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafl Read more…

By Tiffany Trader

A Behind-the-Scenes Look at the Hardware That Powered the Black Hole Image

June 24, 2019

Two months ago, the first-ever image of a black hole took the internet by storm. A team of scientists took years to produce and verify the striking image – an Read more…

By Oliver Peckham

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

Chinese Company Sugon Placed on US ‘Entity List’ After Strong Showing at International Supercomputing Conference

June 26, 2019

After more than a decade of advancing its supercomputing prowess, operating the world’s most powerful supercomputer from June 2013 to June 2018, China is keep Read more…

By Tiffany Trader

Qualcomm Invests in RISC-V Startup SiFive

June 7, 2019

Investors are zeroing in on the open standard RISC-V instruction set architecture and the processor intellectual property being developed by a batch of high-flying chip startups. Last fall, Esperanto Technologies announced a $58 million funding round. Read more…

By George Leopold

Intel Confirms Retreat on Omni-Path

August 1, 2019

Intel Corp.’s plans to make a big splash in the network fabric market for linking HPC and other workloads has apparently belly-flopped. The chipmaker confirmed to us the outlines of an earlier report by the website CRN that it has jettisoned plans for a second-generation version of its Omni-Path interconnect... Read more…

By Staff report

Intel Debuts Pohoiki Beach, Its 8M Neuron Neuromorphic Development System

July 17, 2019

Neuromorphic computing has received less fanfare of late than quantum computing whose mystery has captured public attention and which seems to have generated mo Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This