News

High Performance Computing and deep learning in medicine: Enhancing physicians, helping patients

High Performance Computing and deep learning in medicine: Enhancing physicians, helping patients

Computer-aided diagnosis (CAD) from medical images has been used to help radiologists for many years. But with the use of High Performance Computing and deep learning algorithms for automatic recognition of complicated patterns in magnetic resonance imaging (MRI), computed tomography (CT), or whole-slide histopathology images (WSI), the capabilities of CAD systems have the potential to be significantly improved.

Computers are now, for the first time, matching the performance or even outperforming medical specialists, while also accomplishing the analysis much faster.

Medical Image Analysis (MRI, CT, WSI) are saving lives daily. These techniques are producing an enormous amount of valuable data that needs to be analysed by the physicians that will transform them in diagnosis. But as medical image acquisition methods become more widespread and accessible, the need for radiologists that can interpret an increasing number of images grows as well. For that reason, automated techniques in analysing data are not only very helpful, but are expected to increasingly become the preferred methods to use.

Combining High Performance Computing (HPC) resources with deep learning algorithms could greatly improve the recognition of complicated patterns in MRI, CT, or whole-slide images.  Being able to further develop, optimize, and allow the hospitals and general practitioners to make use of these powerful techniques could have important societal impact by improving the quality of life through more precise and cost effective diagnostics.

The Computational Pathology group of Radboud University Medical Center (Nijmegen, Netherlands) uses High-Performance Computing to enhance their physicians’ diagnostics, providing faster diagnosis and saving more lives.

In the image attached, you can see (left side) an example of colon carcinoma histopathology section stained with Hematoxylin and Eosin (H&E).

On the right side, you can see the result of automatic segmentation (coloring) of multiple tissue types using a deep learning algorithm applied to the digitized image on the left. The algorithm can identify up to 14 different tissue types, which are visualized with different colors according to the color-coding reported in the legend.


Article Source: https://ec.europa.eu/digital-single-market/en/news/high-performance-computing-and-deep-learning-medicine-enhancing-physicians-helping-patients

NEWS​

Related News

ECHAlliance Announces Launch of Thematic Innovation Ecosystem on Cancer led by 4PCAN

20 Feb 2024
On January 25th, Federica Porcu and Radhika Poojora, Innovation Project Manager and Communication Specialist at ECHAlliance - The Global Health Connec...

Introducing our new ecosystem: Singapore Health Technologies Ecosystem! A chat with the Ecosystem Coordinator

19 Feb 2024
Dr Gordon Xiong, Assistant Director at Singapore Health Technologies Consortium (HealthTEC.SG), answered some questions about our very first Singapore...

Digital Health Associates: Unveiling Updates, Introducing New Courses, and Previewing Our Upcoming Summit

19 Feb 2024
As we embark on the journey into 2024, the digitalization of healthcare is no longer a distant dream but a tangible reality reshaping the industry lan...

The Silent Gap: Data Deficiencies in Women's Health

19 Feb 2024
By Federica Porcu, Innovation Project Manager, ECHAlliance - The Global Health Connector
Featured

We welcome our new United States - BioscienceLA Ecosystem! Read the interview with its ecosystem coordinator

19 Feb 2024
We are very excited to welcome our latest ecosystem from the US: Bioscience LA Ecosystem.

Do you have an interest in the West African healthcare market?

19 Feb 2024
Africa Health Business is honored to unveil the 2nd AHB Symposium on Africa Women's Health, themed "A New Dawn of Partnerships for Women's Health".

Become a member

Join ECHAlliance to amplify your organisation’s message, grow your networks, connect with innovators and collaborate globally.
 
First name *
Last Name *
Email Address *
Country *
Position *
First name *
Last Name *
Email Address *
Country *
Position *