Project MONAI Tutorial


September 22nd @ 8:00 AM to 3:00 PM (SGT time)

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.

Learning Goals

After participating in this session, attendees should be able to:


  • Integrate the main components from MONAI Core in your own workflow, including Transforms, Metrics, Losses, Network Architectures, and our new MetaTensor data structure
  • Use MONAI Bundles to help simplify your model development lifecycle throughout MONAI
  • Utilize the MONAI Model Zoo and understand how to contribute a model
  • Understand the new MONAI Federated Learning API
  • Find the latest state-of-the-art techniques, networks, and frameworks for MONAI Core

MONAI Label:

  • Explain the benefits and setup MONAI Label
  • Extend MONAI Label to their own custom application
  • Explain the MONAI Continous Training Loop and Active Learning options

MONAI Deploy:

  • Discuss how MONAI Deploy is bridging the gap from research innovation to clinical production
  • Implement building, verifying, and packaging a deploy-ready AI Application (MAP) using MONAI Deploy App SDK
  • Summarize how MONAI Deploy and the AIDE Platform are being used today


Wenqi Li Photo

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.

Eric Kerfoot Photo

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.

Nic Ma Photo

Engineering Manager, NVIDIA

MONAI Core Lead Developer

Nic ma leads the development of medical deep learning solutions and AI SDK development and optimization, Computer vision, Natural language processing, OCR, etc. Skilled at PyTorch, TensorFlow, Caffe and NVIDIA TensorRT.

Holger Roth Photo

Senior Applied Research Scientist, NVIDIA

MONAI Federated Learning Group Lead

Holger Roth is a Sr. Applied Research Scientist at NVIDIA focusing on deep learning for medical imaging. He has been working closely with clinicians and academics over the past several years to develop deep learning based medical image computing and computer-aided detection models for radiological applications. He is an Associate Editor for IEEE Transactions of Medical Imaging and holds a Ph.D. from University College London, UK. In 2018, he was awarded the MICCAI Young Scientist Publication Impact Award.

Seyed-Ahmad Ahmadi Zephyr Photo

Senior Solutions Architect - Deep Learning in Healthcare, NVIDIA

MONAI Developer

Seyed-Ahmad Ahmadi is a Senior Solution Architect for Deep Learning in Healthcare at Nvidia. 15+ years of experience of working at the cross-section between computer science, clinical research and neuroscience. Skilled in computer science, medical image analysis, clinical data science and applied artificial intelligence (machine and deep learning). Consultation on image processing workflows with state-of-the-art medical imaging toolkits (e.g. MONAI and Nvidia Clara Imaging).

Selnur Erdal Photo

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.

Vikash Gupta Photo

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.

Michael Zephyr Photo

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.



  • Open Science
  • MONAI Overview


  • MONAI Core Architecture and Components
  • MONAI Bundle and Model Zoo
  • MONAI Datasets
  • MONAI Federated Learning API
  • Swin UNETR, AutoML, and other MONAI research


  • MONAI Label Overview
  • MONAI Label App Overview and MONAI Bundle
  • Demo: 3D Slicer + MONAI Label

MONAI Deploy

  • MONAI Deploy Overview
  • MONAI Deploy App SDK - Operators, Bundles, and MAPs
  • MONAI Deploy in the Clinic: An NHS exemplar with AIDE

Want to learn more about MONAI today?

Below you'll find links to the MONAI Website, Tutorials Repo, GitHub Organization, and Slack Channel where you can start to get involved and contribute today.