Visage @ RSNA 2017
Booth #4365, South Hall A
Visage @ Machine Learning Showcase
Kiosk #8149M, North Hall B
Visage @ RSNA 2017
Booth #4365, South Hall A
Visage @ Machine Learning Showcase
Kiosk #8149M, North Hall B
Visage will be demonstrating Enterprise Imaging Excellence at the Radiological Society of North America (RSNA) 2017 in Chicago, IL, November 26 - 30, 2017!
Checkout Visage 7.1.10, the latest version of the Visage 7 Enterprise Imaging Platform, as well as a preview of Visage 7.1.11 at Booth 4365, South Hall A.
Visage is also excited to be one of the exclusive exhibitors in this year's Machine Learning Showcase! Meet with Visage experts in Booth 8149M, North Hall B, to learn more about our exciting artificial intellgence initiatives.
Please enter your contact information to the right, complete with any special requests, and we'll work to schedule you a priority demo. Don't delay, as the schedule fills quickly.
Visage has the proven solutions to deliver success to your imaging enterprise today and the visionary expertise to future proof your informatics investments for tomorrow.
We look forward to seeing you in Chicago!
Visage Imaging is a proven global provider of Enterprise Imaging solutions uniquely enabling the most discriminating, sophisticated imaging organizations in the world. The Visage® 7 Enterprise Imaging Platform delivers amazingly fast server-side rendered images, streamed via an intelligent smart-client viewer. Leveraging a One Viewer® philosophy, radiologists and referring physicians have a customized, protocol-driven workflow to natively view multi-dimensional imagery across a single desktop. As an Enterprise Imaging Platform, Visage 7 also offers the Visage 7 Open Archive, supports non-DICOM and medical multimedia objects, as well as provides diagnostic mobile access with Visage Ease Pro™. Visage 7 is future-proof powered by NVIDIA GPUs, with our platform approach to artificial intelligence, Visage 7 AI* (works-in-progress), to further enhance best-in-class In-Viewer Workflow with integration to native and third-party algorithms.
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