1 / 13

Building a Stronger Future for Radiology: Takeaways from RSNA 2017

At RSNA 2017, NVIDIA announced partnerships, showcased the latest technologies revolutionizing medical imaging, offered NVIDIA Deep Learning Institute (DLI) workshops and more.

nvidia
Download Presentation

Building a Stronger Future for Radiology: Takeaways from RSNA 2017

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. BUILDING A STRONGER FUTURE Takeaways from RSNA 2017 FOR RADIOLOGY

  2. RSNA 2017: Explore. Invent. Transform.

  3. This year’s 103rdAnnual Meeting of the Radiological Society of North America (RSNA) brought together the largest gathering of radiologists and medical physicists. More than 55,000 attended.

  4. RSNA 2017: AN OVERVIEW At RSNA 2017, NVIDIA announced new key partnerships, showcased the latest technologies revolutionizing medical imaging, offered NVIDIA Deep Learning Institute (DLI) workshops and much more. “As healthcare professionals strive to increase their efficiency to serve an ever-growing population, the industry is turning to AI and machine learning as essential tools to improve productivity and patient outcomes. And NVIDIA is playing a leading role in that effort.” READ BLOG Source: https://blogs.nvidia.com/blog/2017/11/26/ai-medical-imaging/

  5. NVIDIA PARTNERS WITH GE HEALTHCARE To kick off RSNA 2017, NVIDIA announced the first of two major partnerships. The collaboration with GE Healthcare brings NVIDIA’s AI computing platform to GE’s 500,000 imaging devices. “GE announced the new NVIDIA GPU-powered Revolution Frontier CT, a CAT scan system that is ‘two times faster in imaging processing than its predecessor, due to its use of NVIDIA’s AI computing platform.’” READ ARTICLE Source: https://www.forbes.com/sites/davealtavilla/2017/11/28/nvidia-and-ge-partner-to-bring-ai-assisted-data-analytics-and-visualization-to-healthcare/#4b4155e31309

  6. THE IMPACT OF AI IN HEALTHCARE A trending topic throughout RSNA 2017 was machine learning applications for radiology, and AI continues to open the door for intelligent medical instruments. “The combination of deep learning, NVIDIA GPU computing and medical imaging is spurring a new age of intelligent medical instruments. Pioneers in the diagnostic imaging community have jumped on the NVIDIA GPU platform to achieve amazing results in each of the major stages of the medical imaging pipeline — reconstruction, image processing and visualization.” READ BLOG Source: https://blogs.nvidia.com/blog/2017/11/26/intelligent-medical-instruments/

  7. NVIDIA PARTNERS WITH NUANCE On Day 2 of RSNA 2017, NVIDIA announced its second partnership: bringing the NVIDIA AI platform to Nuance’s AI Marketplace. “The AI Marketplace combines the power of NVIDIA’s deep learning platform with Nuance’s PowerScribe radiology reporting and PowerShare image exchange network, used by 70% of all radiologists in the U.S. The Nuance AI Marketplace is designed to be a prime source for imaging algorithms that augment the capabilities of radiologists and provide rapid, open access to the industry’s most advanced research.” READ ARTICLE Source: http://hitconsultant.net/2017/11/27/nuance-artificial-intelligence-marketplace/

  8. CUTTING-EDGE AI DEMOS Our booth featured demos that covered volumetric segmentation, cinematic rendering for visualization and image reconstruction. One featured demo from the A.A. Martinos Center for Biomedical Imaging presents a data-driven unified image reconstruction approach: AUTOMAP. “Image reconstruction plays a critical role in the implementation of all contemporary imaging modalities across the physical and life sciences… We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for a variety of MRI acquisition strategies.” VIEW PAPER Source: https://arxiv.org/pdf/1704.08841.pdf

  9. THE INAUGURAL DEEP LEARNING CLASSROOM The RSNA Deep Learning Classroom, presented by NVIDIA Deep Learning Institute (DLI), trained over 1000 people during the 5 day span of the show. Workshops included hands-on training in image classification, image segmentation, quantitative imaging and Radiomics. Missed it? Visit the DLI website for self-paced online labs. VIEW ONLINE LABS

  10. INCEPTION HEALTHCARE MEMBERS Less than 18 months after its launch, NVIDIA’s Inception program — which helps accelerate startups pushing the frontiers of AI and data science — has signed up its 2,000th member company. NVIDIA currently has over 100 AI healthcare startups in our Inception program. Several shared their cutting-edge technology and demos in the NVIDIA booth—including Aidence, 16 Bit, DesAcc, Vuno, LPixel, CureMetrix, Lunit, Quantib, Aidoc, and RadLogics . LEARN MORE

  11. CASE STUDY: DEEP LEARNING MODELS IN THE HOSPITAL As the field of deep learning in medicine progresses from research to clinical deployment, practical considerations quickly become a primary concern for operational leadership. See how MGH & BWH Center for Clinical Data Science approaches building a hardware infrastructure on-premises. “Our goal is to help other teams jumpstart their hardware efforts as they seek to implement deep learning in a hospital environment.” VIEW PAPER Source: http://www.nvidia.com/object/developing-deep-learning-models-in-the-hospital

  12. PARTING THOUGHTS: AI IN MEDICAL IMAGING “From precision imaging and Easy PACS to 3-D viewing, cloud technology and machine learning, radiology is indeed at the forefront of technology and innovation in medicine.” “…AI algorithms are deriving new information from sometimes very low quality image data, and may someday change the way imaging devices are designed.” “Radiology needs universally accepted ways to develop and incorporate AI, similar to the DICOM image standard, in order to make it easy for developers to create new applications and integrate them into imaging devices and clinical information systems.” WHERE IS AI HEADED? Sources: https://rsna2017.rsna.org/dailybulletin/pdf/TEC_wed.pdf, https://rsna2017.rsna.org/dailybulletin/index.cfm?pg=17tue01, https://rsna2017.rsna.org/dailybulletin/index.cfm?pg=17wed02

  13. To learn more about NVIDIA in healthcare, visit: http://www.nvidia.com/healthcare

More Related