I was able to attend the Radiological Society of North America (RSNA) meeting at McCormick Place in Chicago, IL, which occurred from November 26 to December 1, 2017. The annual meeting is a very large gathering of industry leaders in medical imaging, radiologists, and other related industry professionals. This was the 103rd Scientific Assembly and Annual Meeting with the tagline: Explore, Invent, Transform. This year the meeting was heavily focused on topics around machine learning, virtual reality, and 3D printing. Like always, there were lots of exhibitors with many new medical imaging devices ready to discuss and provide demonstrations. There were also interesting plenary sessions, educational courses, and scientific sessions. Furthermore, there were numerous posters and presentations.
A popular feature this year at RSNA, was a deep learning classroom presented by the NVIDIA Deep Learning Institute (DLI), designed for attendees to engage with machine learning tools, write algorithms, and improve their understanding of emerging machine learning technology. In one of these sessions, attendees trained a deep neural network to recognize handwritten digits. In another session, attendees trained Convolutional Neural Networks (CNNs) to create biomarkers to identify the genomics of a disease without the use of an invasive biopsy. In yet another session, attendees segmented MRI images to measure parts of the heart.
Another feature this year was a separate section for Machine Learning Showcase Exhibitors. This section allowed those interested in machine learning to easily network with those in the field. This section featured a Machine Learning Theatre with presentations from industry leaders. For example, in one presentation, Google Cloud talked about machine learning in imaging and how to build your own models on the cloud. In another presentation, Siemens Healthineers discussed artificial intelligence solutions for clinical decision making by turning medical images into biomarkers to help increase effectiveness of care. There was also a 3D Printing Theater with many posters and actual 3D printed parts nearby. In addition, there were several virtual reality demos setup to allow attendees to try themselves.
I was able to attend many interesting courses on machine learning, radiomics, 3D printing, virtual reality, and predictive analytics. For example, in one course I attended there was discussion of how to use KNIME to incorporate radiology data sources into predictive modeling and interpret the results and make visualizations. There was an interesting talk in another course I attended about using virtual reality in medical education and how it can greatly decrease the amount of time needed to teach students when compared to PowerPoint presentations. In yet another course I attended, instructors walked attendees through using Mimics and 3-matic from Materialise. In this course participants were taught how to segment out musculoskeletal, body, neurological, and vascular systems from DICOM files into a Standard Tessellation Language (STL) file for use with a 3D printer.
I was also able to attend the plenary session by Michio Kaku titled The Next 20 Years: How science and technology will revolutionize business, the economy, jobs, and our way of life. In the talk Dr. Kaku discussed the next wave of wealth generation in our modern economy which he believes is advancements at the molecular level including in artificial intelligence, nanotechnology, and biotechnology linked together by the cloud. He believes that information will be everywhere and computers will become like the word electricity today, where it is not mentioned in language as it is ubiquitous. Dr. Kaku recognized robots will replace jobs in the future but said robots are weak in three areas: 1) pattern recognition, 2) common sense, and 3) human interactions. Thus he believes in many cases artificial intelligent systems will aid humans and not replace them.
Below are some of the pictures I took while at the RSNA annual meeting in 2017, in Chicago, IL.