Developing for the Medical AI Project Lifecycle with MONAI
MONAI is the Medical Open Network for AI. Project MONAI was created with the goal of accelerating the pace of research and development by providing a common software foundation and a vibrant community for medical imaging deep learning.
MONAI is targeted for both the medical image computing (MIC) and the computer-assisted intervention (CAI) communities. It is an extension to PyTorch that includes specialized deep learning methods for diagnostic radiology, surgical planning, medical robotics, image-guided interventions, and nearly every other aspect of patient care.
MONAI provides a customizable interface to bring components to existing training pipelines or perform end-end training. Its goal is to increase efficiency and collaborations amongst researchers by providing domain-optimized foundational capabilities and standardization around algorithms’ benchmarking, evaluation, and reproducibility. These goals help improve reproducibility by not only being used as a framework for development but comparison, evaluation, and benchmarking by bringing together the community and organically converging on a standard set of best practices.
After participating in this session, attendees should be able to:
Senior Applied Research Scientist, NVIDIA
Lead Architect for MONAI Core
Wenqi Li is a senior applied research scientist at NVIDIA, focusing on computer vision and machine learning techniques for medical image analysis. Wenqi received his Ph.D. in applied computing from The University of Dundee (Scotland, UK) in 2015.
Software Architect in Medical Engineering, King's College London
MONAI Core Lead Developer
Eric Kerfoot is an experienced research software engineer with a demonstrated history of working in scientific research and scientific software development. Strong professional skilled in Mathematical Modeling, 3D Visualization, Python, Computer Science, Deep Learning, and Data Analysis.
Post-Doctoral Researcher, DKFZ
Metrics Reloaded Developer
Annika Reinke joined the division of Intelligent Medical Systems at the German Cancer Research Center (DKFZ) to adapt mathematical concepts to societally relevant topics, like scientific benchmarking and validation. Having published disruptive findings on biomedical image analysis challenges in Nature Communications, she is a founding member of the initiative of Biomedical Image Analysis ChallengeS (BIAS) aiming for bringing biomedical image analysis challenges to the next level of quality. She serves as the secretary of the MICCAI special interest group on biomedical challenges and as an active member and taskforce lead of the MONAI working group on evaluation, reproducibility and benchmarking.
Technical Marketing Engineer, NVIDIA
MONAI Label Co-Creator
Andres is a multidisciplinary machine learning & deep learning researcher with 5+ years of experience developing AI systems for healthcare applications. He is the co-creator of MONAI Label. Currently, a Technical Marketing Engineer at NVIDIA and a Senior Visiting Research Fellow at King's College London.
Applied Research Scientist, NVIDIA
Dong Yang is a senior applied research scientist at AI-Infra of NVIDIA. He specializes in medical image processing, and is currently working on deep learning methods to solve medical imaging problems, with the goal of improving the effectiveness of clinical workflows.
Technical Director, Center for Augmented Intelligence in Imaging Department of Radiology, Mayo Clinic
MONAI Deploy Group Lead
Dr. Erdal received his Ph.D. in Electrical and Computer Engineering from The Ohio State University and currently serves as Technical Director for the Center for Augmented Intelligence in Imaging, Department of Radiology at Mayo Clinic.
Assistant Professor of Radiology, Mayo Clinic
MONAI Deploy Lead Developer
Vikash Gupta is a data science analyst at the Department of Radiology, Mayo Clinic Florida. He works on building tools for deep learning and radiology. Prior to Mayo Clinic, he worked as a research scientist at The Ohio State University. His research interests and publications include data augmentation techniques for medical imaging, democratizing AI for healthcare, cardiac computed tomography, X-rays, mammography, fracture detection and super resolution for diffusion weighted images.
Developer Evangelist, NVIDIA
MONAI Adoption and Outreach Lead
Michael Zephyr is a developer evangelist on the NVIDIA Clara team and is responsible for helping design the developer experience for all NVIDIA Clara products. He develops and presents content through blog posts, webinars, and conferences that help developers quickly get started. Michael received his master's in computer science specializing in machine learning and interactive intelligence from Georgia Tech University.