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Primeur weekly 2017-05-15

Special

NVIDIA ushers in new era of robotics, with breakthroughs making It easier to build and train intelligent machines ...

NVIDIA and Toyota collaborate to accelerate market introduction of autonomous cars ...

NVIDIA launches GPU Cloud platform to simplify AI development ...

NVIDIA advances AI computing revolution with new Volta-based DGX systems ...

NVIDIA launches revolutionary Volta GPU platform, fueling next era of AI and high performance computing ...

NVIDIA to train 100,000 developers on deep learning in 2017 ...

NVIDIA paves path to AI cities with Metropolis Edge-to-Cloud platform for video analytics ...

NVIDIA Tesla accelerators on IBM Cloud demonstrate advanced performance for training deep learning models ...

BSC and NVIDIA a step forward to the interactive simulation of humans ...

Quantum computing

Refrigerator for quantum computers discovered ...

Focus on Europe

EoCoE service page open for PRACE user community ...

Spring 2017 edition of the e-IRG newsletter available ...

PRACE to issue Annual Report 2016 ...

Middleware

Bright Computing announces 8.0 release - Setting new standard for automation and ease-of-use for Linux-based clusters and public, private and hybrid Clouds ...

CETIAT France chooses Bright Cluster Manager for aerodynamics and fluid mechanics HPC environment ...

2016 National Research Infrastructure Roadmap for Australia is now available ...

Hardware

Cray delivers production-ready AI with new Cray CS-Storm accelerated cluster supercomputers ...

Inspur to unveil 2U 8-GPU AI supercomputer at GTC 2017 ...

Inspur unveiled AIStation, AI deep learning training cluster management software at GTC 2017 ...

Supermicro systems deliver 170 Tflop/s FP16 of peak performance for artificial intelligence, and deep learning at GTC 2017 ...

NCSA's Blue Waters project provides $1.08 billion direct return to Illinois' economy ...

Applications

SDSC's Comet helps replicate brain circuitry to direct a realistic prosthetic arm ...

University of Wyoming graduate student one of 36 selected for CyberGIS Summer School ...

Supercomputing mimics berkelium experiments to validate new find ...

'Inverse designing' spontaneously self-assembling materials ...

Story of silver birch from genomic Big Data ...

Targeted, high-energy cancer treatments get a supercomputing boost ...

Computer accurately identifies and delineates breast cancers on digital tissue slides ...

Sound over silicon: Computing's wave of the future ...

USC Viterbi School of Engineering faculty awarded multiple MURI grants ...

New funding announced for Digital Earth Australia ...

NCSA releases annual report highlighting scientific exploration and breakthroughs enabled by the Blue Waters Project ...

Supercomputer can disprove the theory of sunspot formation ...

The Cloud

Inspur to release InCloud OS 5.0 "F.A.S.T" at the 2017 OpenStack Summit ...

IBM extends data science collaborative workspace to the private Cloud ...

Pointwise and Envenio Join Forces on Demand ...

Computer accurately identifies and delineates breast cancers on digital tissue slides

This is a tumour boundary delineated by a pathologist. Credit: Anant Madabhushhi.10 May 2017 Cleveland - A deep-learning computer network developed through research led by Case Western Reserve University was 100 percent accurate in determining whether invasive forms of breast cancer were present in whole biopsy slides. Looking closer, the network correctly made the same determination in each individual pixel of the slide 97 percent of the time, rendering near-exact delineations of the tumours.

Compared to the analyses of four pathologists, the machine was more consistent and accurate, in many cases improving on their delineations.

In a field where time and accuracy can be critical to a patient's long-term prognosis, the study is a step toward automating part of biopsy analysis and improving the efficiency of the process, the researchers said.

Currently, cancer is present in one in 10 biopsies ordered by physicians, but all must be analyzed by pathologists to identify the extent and volume of the disease, determine if it has spread and whether the patient has an aggressive or indolent cancer and needs chemotherapy or a less drastic treatment.

Last month, the U.S. Food and Drug Administration approved software that allows pathologists to review biopsy slides digitally to make diagnosis, rather than viewing the tissue under a microscope.

"If the network can tell which patients have cancer and which do not, this technology can serve as triage for the pathologist, freeing their time to concentrate on the cancer patients", stated Anant Madabushi, F. Alex Nason professor II of biomedical engineering at Case Western Reserve and co-author of the study detailing the network approach, published in Scientific Reports .

To train the deep-learning network, the researchers downloaded 400 biopsy images from multiple hospitals. Each slide was approximately 50,000 x 50,000 pixels. The computer navigated through or rectified the inconsistencies of different scanners, staining processes and protocols used by each site, to identify features in cancer versus the rest of the tissue.

The researchers then presented the network with 200 images from The Cancer Genome Atlas and University Hospitals Cleveland Medical Center. The network scored 100 percent on determining the presence or absence of cancer on whole slides and nearly as high per pixel.

"The network was really good at identifying the cancers, but it will take time to get up to 20 years of practice and training of a pathologist to identify complex cases and mimics, such as adenosis", stated Anant Madabhushi, who also directs the Center of Computational Imaging and Personalized Diagnostics at Case Western Reserve.

Network training took about two weeks, and identifying the presence and exact location of cancer in the 200 slides took about 20 to 25 minutes each.

That was done two years ago. Anant Madabhushi suspects training now - with new computer architecture - would take less than a day, and cancer identification and delineation could be done in less than a minute per slide.

"To put this in perspective", Anant Madabhushi stated, "the machine could do the analysis during 'off hours', possibly running the analysis during the night and providing the results ready for review by the pathologist when she/he were to come into the office in the morning."

Anant Madabhushi worked with Angel Cruz-Roa, a PhD student, and Fabio Gonzalez, professor, Department of Systems and Industrial Engineering at the Universidad Nacional de Colombia, in Bogota; Hannah Gilmore, associate professor of pathology at Case Western Reserve School of Medicine; Ajay Basavanhally of Inspirata Inc., Tampa, Florida; Michael Feldman, professor of pathology and laboratory medicine, and Natalie Shi, of the Department of Pathology, at the Hospital of the University of Pennsylvania; Shridar Ganesan, associate professor of medicine and pharmacology at the Rutgers Cancer Institute of New Jersey; and John Tomaszewski, chair of pathology and anatomical services at the University of Buffalo, State University of New York.

Much of the study was built on research by Anant Madabhushi and Andrew Janowczyk, a biomedical engineering Postdoctoral Fellow at Case Western Reserve. They led development of what they termed "a resolution adaptive deep hierarchical learning scheme", which can cut the time for image analysis using deep learning approaches by 85 percent.

Deep-learning networks learned to identify indicators of cancer at lower resolutions to determine where further analysis at high levels of magnification, and thus greater computation time, were necessary to provide precise results. In short, the scheme eliminated time-consuming, high-resolution analysis of healthy tissue.

To manage the variance in staining of digitized biopsy images that can confound computer analysis, the researchers developed a technique called Stain Normalization using Sparse AutoEncoders. The technique partitions images into tissue sub-types so colour standardization for each can be performed independently.

To speed research in the field, Andrew Janowczyk and Anant Madabhushi also published a tutorial on deep learning for digital pathology image analysis. The paper was recently awarded the most cited paper award from theJournal of Pathology Informatics.

Source: Case Western Reserve University

Back to Table of contents

Primeur weekly 2017-05-15

Special

NVIDIA ushers in new era of robotics, with breakthroughs making It easier to build and train intelligent machines ...

NVIDIA and Toyota collaborate to accelerate market introduction of autonomous cars ...

NVIDIA launches GPU Cloud platform to simplify AI development ...

NVIDIA advances AI computing revolution with new Volta-based DGX systems ...

NVIDIA launches revolutionary Volta GPU platform, fueling next era of AI and high performance computing ...

NVIDIA to train 100,000 developers on deep learning in 2017 ...

NVIDIA paves path to AI cities with Metropolis Edge-to-Cloud platform for video analytics ...

NVIDIA Tesla accelerators on IBM Cloud demonstrate advanced performance for training deep learning models ...

BSC and NVIDIA a step forward to the interactive simulation of humans ...

Quantum computing

Refrigerator for quantum computers discovered ...

Focus on Europe

EoCoE service page open for PRACE user community ...

Spring 2017 edition of the e-IRG newsletter available ...

PRACE to issue Annual Report 2016 ...

Middleware

Bright Computing announces 8.0 release - Setting new standard for automation and ease-of-use for Linux-based clusters and public, private and hybrid Clouds ...

CETIAT France chooses Bright Cluster Manager for aerodynamics and fluid mechanics HPC environment ...

2016 National Research Infrastructure Roadmap for Australia is now available ...

Hardware

Cray delivers production-ready AI with new Cray CS-Storm accelerated cluster supercomputers ...

Inspur to unveil 2U 8-GPU AI supercomputer at GTC 2017 ...

Inspur unveiled AIStation, AI deep learning training cluster management software at GTC 2017 ...

Supermicro systems deliver 170 Tflop/s FP16 of peak performance for artificial intelligence, and deep learning at GTC 2017 ...

NCSA's Blue Waters project provides $1.08 billion direct return to Illinois' economy ...

Applications

SDSC's Comet helps replicate brain circuitry to direct a realistic prosthetic arm ...

University of Wyoming graduate student one of 36 selected for CyberGIS Summer School ...

Supercomputing mimics berkelium experiments to validate new find ...

'Inverse designing' spontaneously self-assembling materials ...

Story of silver birch from genomic Big Data ...

Targeted, high-energy cancer treatments get a supercomputing boost ...

Computer accurately identifies and delineates breast cancers on digital tissue slides ...

Sound over silicon: Computing's wave of the future ...

USC Viterbi School of Engineering faculty awarded multiple MURI grants ...

New funding announced for Digital Earth Australia ...

NCSA releases annual report highlighting scientific exploration and breakthroughs enabled by the Blue Waters Project ...

Supercomputer can disprove the theory of sunspot formation ...

The Cloud

Inspur to release InCloud OS 5.0 "F.A.S.T" at the 2017 OpenStack Summit ...

IBM extends data science collaborative workspace to the private Cloud ...

Pointwise and Envenio Join Forces on Demand ...