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

Dive into the Future of Healthcare with the Essentials of Digital Health Certificate Course!

18 Jul 2024
“You are going to hit a zone of professional irrelevance if you do not know Digital Health.”  ~ Dr. Rajendra Pratap Gupta, Chairman, Academy of Digita...

The report for the 2nd Africa Women’s Health Symposium is now available!

17 Jul 2024
For over half a decade, Africa Health Business has convened stakeholders from across the African health sector to address and find solutions to critic...

Join the Africa Digital Health Revolution with ADHN

17 Jul 2024
The Africa Digital Health Network (ADHN) invites all stakeholders in health and technology to join us in revolutionizing healthcare across the contine...

Have your say: Open Public Consultation for the BeWell Skills Strategy

17 Jul 2024

Fit 4 Start #15 open to healthtech, digital, and space startups

16 Jul 2024
For its 15th edition, Luxembourg’s startup accelerator Fit 4 Start is accepting applications from innovative healthtech, digital, and space enterprise...

Rivne Interregional Medical Cluster became a member of the project consortium PRECISEU

16 Jul 2024
Ukraine, represented by the Rivne Interregional Medical Cluster, became a member of the project consortium PRECISEU

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 *