How cloud AI infrastructure allows radiotherapy breakthroughs at Elekta

Introduced by Microsoft + NVIDIA

Regardless of a bunch of challenges, among the most profitable examples of transferring modern AI functions into manufacturing come from healthcare. On this VB Highlight occasion, learn the way organizations in any business can comply with confirmed practices and leverage cloud-based AI infrastructure to speed up their AI efforts.

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From pilot to manufacturing, AI is a problem for each business. However as a extremely regulated, high-stakes sector, healthcare faces particularly complicated obstacles. Cloud-based infrastructure that’s “purpose-built” and optimized for AI has emerged as a key basis of innovation and operationalization. By leveraging the pliability of cloud and high-performance computing (HPC), enterprises in each business are efficiently increasing proof of ideas (PoC) and pilots into manufacturing workloads.

VB Highlight introduced collectively Silvain Beriault, AI technique lead and lead analysis scientist at Elekta, a high international innovator of precision radiotherapy methods for most cancers remedy and John Ok. Lee, AI platform and infrastructure principal lead at Microsoft Azure. They joined VB Consulting Analyst Joe Maglitta to debate how cloud-based AI infrastructure has pushed improved collaboration and innovation for Elekta’s worldwide R&D efforts aimed toward bettering and increasing the corporate’s mind imaging and MR-guided radiotherapy throughout the globe.

The large three advantages

Elasticity, flexibility and ease high the advantages of end-to-end, on-demand, cloud-based infrastructure-as-a-service (IaaS) for AI, in keeping with Lee. 

As a result of enterprise AI sometimes begins with a PoC, Lee says, “cloud is an ideal place to start out. You will get began with a single bank card. As fashions turn out to be extra complicated and want for added compute capability will increase, cloud is the right place to scale that job.”  That features scaling up or rising the variety of GPUs interconnected to a single host to extend the capability of the server and scaling out or elevating the variety of host situations to extend the general system efficiency.

Cloud’s flexibility lets organizations handle workloads of any dimension, from huge enterprise tasks to smaller efforts that want much less processing energy. For any sized effort, purpose-built cloud infrastructure companies ship far quicker time-to-value and higher TCO and ROI than constructing on-premises AI structure from scratch, Lee explains.

As for simplicity, Lee says pre-tested, pre-integrated, pre-optimized {hardware} and software program stacks, platforms, growth environments and instruments make it straightforward for enterprises to get began.

COVID accelerates Elekta’s cloud-based AI journey

Elekta is a medical know-how firm growing image-guided scientific options for the administration of mind problems and improved most cancers care. When the COVID pandemic pressured researchers out of their labs, firm leaders noticed a possibility to speed up and develop efforts to shift AI R&D to the cloud which had begun just a few years earlier.

The division’s AI head knew a extra sturdy, accessible cloud-based structure to enhance its array of AI-powered options would assist Elekta advance its mission of accelerating entry to healthcare, together with under-served international locations.

By way of value evaluation, Elekta additionally knew it will be troublesome to estimate present and future wants when it comes to high-performance computing. They thought-about the price of sustaining on-prem infrastructure for AI and its limitations. The general expense and complexity prolong far past buying GPUs and servers, Beriault notes.

“Attempting to do this by your self can get exhausting fairly quick. With a framework like Azure and Azure ML, you get far more than entry to GPUs,” he explains. “You get a complete ecosystem for doing AI experiments, documenting your AI experiments, sharing information throughout totally different R&D facilities. You’ve a typical ML ops device.”

The pilot was easy: automating the contouring of organs in MRI photos to speed up the duty of delineating the remedy goal, in addition to organs in danger to spare from radiation publicity.

The power to scale up and down was essential for the undertaking. Up to now, “there have been instances the place we’d launch as a lot as ten coaching experiments in parallel to do some hyper-parameter tunings of our mannequin,” Beriault recollects. “Different instances, we have been simply ready for information curation to be prepared, so we wouldn’t practice in any respect. This flexibility was crucial for us, provided that we have been, on the time, fairly a small group.”

For the reason that firm already used the Azure framework, they turned to Azure ML for his or her infrastructure, in addition to essential help as groups realized to make use of the platform portal and APIs to start launching jobs within the cloud. Microsoft labored with the group to construct an information infrastructure very particular to the corporate’s area and handled essential information safety and privateness points.

“As of at present, we’ve expanded on auto-contouring, all utilizing cloud-based methods. Utilizing this infrastructure has allowed us to develop our analysis actions to greater than 100 organs for a number of tumor websites. What’s extra, scaling has allowed us to develop to different extra complicated AI analysis in RT past easy segmentation, rising the potential to positively impression affected person remedies sooner or later.”

Choosing the proper infrastructure accomplice

In the long run, Beriault says adopting cloud-based structure lets researchers give attention to their work and develop the absolute best AI fashions as an alternative of constructing and “babysitting” AI infrastructure.

Selecting a accomplice who can present that form of service is essential, Lee commented. A robust supplier should convey sturdy strategic partnership that helps hold its services and products on the innovative. He says Microsoft’s collaboration with NVIDIA to develop foundations for enterprise AI can be crucial for purchasers like Elekta. However there are different issues, he provides.

“You ought to be reminding your self, it’s not simply concerning the product choices or infrastructure. Have they got the entire ecosystem? Have they got the neighborhood? Have they got the proper individuals that can assist you?”

Register to observe on-demand now!


  • First-hand expertise and recommendation about the very best methods to speed up growth, testing, deployment and operation of AI fashions and companies
  • The essential function AI infrastructure performs in transferring from POCs and pilots and into manufacturing workloads and functions
  • How a cloud-based, “AI-first method” and front-line-proven finest practices will help your group, no matter business, extra rapidly and successfully scale AI throughout departments or the world

Audio system

  • Silvain Beriault, AI Technique Lead and Lead Analysis Scientist, Elekta
  • John Ok. Lee, AI Platform & Infrastructure Principal Lead, Microsoft Azure
  • Joe Maglitta, Host and Moderator, VentureBeat

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