There should be no question that the world sits on the precipice of a major sea change where () and () will be as pervasive as air and infused into our lives, similar to the way the Internet has. can analyze information, find anomalies and make informed decisions faster than people can and will be an important tool to help us do our jobs better.
However, the adoption of varies widely by vertical. One of the industries that has embraced is healthcare, as it can have lifesaving consequences.
A great example of this is the work that the Boston-based MGH & BWH Center for Clinical Data Science — a collaboration by Mass General Hospital (MGH) and Brigham and Women’s Hospital (BWH) to create, promote and commercialize for healthcare — has been doing to accelerate the process of analyzing magnetic resonance images (MRIs).
I recently talked with Neil Tenenholtz, senior scientist at the MGH & BWH Center for Clinical Data Science (CCDS), to better understand what the organization is doing and how it is leveraging the NVIDIA DGX Station to power their initiatives. (Note: NVIDIA is a client of ZK Research.)
DGX Station brings data center power to the desktop
DGX Station can be thought of as a desktop-size, GPU-enabled super-computer. The primary platform from NVIDIA had been the DGX-1, a rack-mountable server that holds eight NVIDIA Tesla V100 GPUs. The challenge with a data center-housed server is that a data scientist would need to work with IT to have the DGX-1 provisioned for that particular task. This usually creates a lag time that could get in the way of the researcher being able to analyze data and conduct experiments.
DGX Station is a portable, workstation form factor that is powered by 4 Tesla V100 GPUs. Although the form factor is compact, the compute power of a 4 GPU system is the equivalent to approximately 400 x86 CPUs, making DGX Station ideal of computationally intensive processes such as , and data analytics.
can improve radiologist efficiency
The mission of the group Tenenholtz works for is to develop -based models specifically for radiology and integrate them into the radiologists’ clinical workflow. Successful models can be licensed to external partners or spun out and commercialized through startup, so they may be used by hospitals all over the world. […]