Arlington Imaging Artificial Intelligence (AI-AI) Workshop
Workshop Presentations (Click on Topic for PDF File of Presentation)
Session 1: Understanding the Problems
Steve Horii, MD - Challenges for Artificial/Machine Intelligence for Medical Imaging: “Will Radiology ever disappear as a Specialty?"
Ismail Baris Turkbey, PhD - Three Generations of AI-assisted Prostate Cancer Detection on mpMRI: "What We learned from Radiologist-AI Interaction"
Jia-Bin Huang, PhD - Visual Learning from Few Labeled Data
In K. Mun PhD - Surgical Robot: "Will AI Driven Robot Replace a Surgeon?"
Session 2: Science and Technology of Machine Learning
Nathan Lay, PhD - Gaps and Limitations of Convolutional Neural Networks and Possible Implications
ShihChung Benedict Lo, PhD - Convolutional Neural Networks in Medical and General Image Pattern Recognition
Furong Huang, PhD - Tensorial Neural Networks (TNN)
Esteban Rubens - AI and Imaging: "Your Data as a Strategic Asset"
Luncheon Speaker Session:
Steve Worrell - Making Clinical AI Relevant
Session 3: Institutional Activities Enhancing Machine Learning Research
Nicholas Petrick, PhD - FDA Regulatory Framework and Evaluation Methods for AI/ML-Based Decision Tools
Rick Avila - New Data Resources for Accelerating AI Research
Peter Li - Open Source Strategy and Open Collaboration for CNN Tools
Session 4: Enabling Next Generations Machine Learning Research
Thomas Sanford, MD - Building the Standard: Data Curation for All-in-One Detection, Segmentation, and Risk Scoring
Fred Prior, PhD - Curating High Quality, Open Access Data to Enable Machine Learning Research
Stephanie Harmon, PhD - Ground Truth Labeling: "Can Digital Pathology AI be used to Improve Radiology AI?"
Session 5: Shaping the Future Panel Discussion
Peter Choyke, MD - Radiology 2050: "Will You Even Recognize It?"