RSNA Announces AI and Machine-Learning Initiatives for 2018

The Radiological Society of North America {(RSNA) Oak Brook, IL, USA} has planned a diverse roster of machine learning (ML) and artificial intelligence (AI) programming for RSNA 2018 that will focus on the power and potential of AI in radiology and issues associated with implementation.

The RSNA’s 104th Scientific Assembly and Annual Meeting is scheduled to be held on November 25-30 at McCormick Place in Chicago, Illinois, USA. Attendees will be able to experience the hands-on cutting-edge technology of AI, 3D printing and virtual reality, delve into the latest research, and enhance their skills with a course program packed with plenary sessions and over 400 top-level educational courses. RSNA has planned expanded learning and research opportunities for RSNA 2018, including leading-edge presentations, return of the RSNA ML Challenge and the ML Showcase.

Following the debut of the ML Pediatric Bone Age Challenge in 2017, RSNA 2018 will feature the ML Pneumonia Detection Challenge. In the 2017 challenge, more than 250 participants created algorithms to predict skeletal age using pediatric hand X-rays. The 2018 ML Challenge invites participants to develop tools that identify and localize pneumonia on chest X-rays using images from a publicly available National Institutes of Health (NIH) data set. For this year’s challenge, a set of 30,000 images has been annotated by a team of volunteer radiologists. The contesting teams will use the annotations, which identify abnormal areas in the lung images and assess the probability of pneumonia, to develop their algorithms. The challenge will launch in August with the release of the “training” dataset, while the evaluation phase will be held in October. The most accurate algorithm submissions will be recognized in the ML Showcase. The ML Showcase at this year’s event will see twice as much space focused on the latest developments in ML and AI software and products, and offer insights into this game-changing technology from the industry leaders as well as networking opportunities with leading companies.

NVIDIA Deep Learning Institute (DLI), a leader in visual computing technologies, will also return to this year’s event by presenting the RSNA Deep Learning Classroom. Certified instructors from NVIDIA’s DLI will help attendees learn to write algorithms and improve their understanding of AI technology. At the 2018 classroom, there will be increased focus on radiology imaging with advanced topics such as data augmentation, segmentation and multi-parametric classification.

Along with AI-focused refresher courses and scientific sessions, the RSNA 2018 offers a variety of other educational experiences focusing on AI research. Radiologists will be able to visit the National Cancer Institute’s Crowds Cure Cancer exhibit returning for its second year. Presented in the Learning Center, the project invites radiologists to annotate clinical images for ML research.

“In the years to come, RSNA’s support for education, research and innovation in this field will grow as AI becomes an integral part of radiology practice. RSNA will continue to educate not only radiologists, but also researchers and industry scientists about AI and ML," said Dr. Langlotz, a professor of radiology and biomedical informatics and director of the Center for Artificial Intelligence in Medicine and Imaging in the Department of Radiology at Stanford University.

Related Links:
Radiological Society of North America