Hello Today speaking about the MONAI toolkit, a platform for the application of deep learning to medical image analysis
However, the goal of this talk is much broader. My goal is to spark your interest in open science, and in particular, show you the value of open source software for deep learning.
For my talk I will first, provide a brief overview of why deep learning is succeeding in our field. Second, I will then provide a brief history of open science Third, I will present How you and MONAI can work together to both benefit from and contribute to open science. That is, show you the value of using MONAI.
The benefits of open science led to the creation of MONAI.
MONAI Medical Image Deep Learning: A 3-Minute Introduction
The Open Source Platform
for Reproducible Deep Learning
in Medical Imaging
Stephen R. Aylward, Ph.D.
Chair of MONAI External Advisory Board
Senior Directory of Strategic Initiatives, Kitware
Medical Open Network for A. I. (MONAI)
Goal: Accelerate the pace of research and development
by providing a common software foundation and
a vibrant community for medical imaging deep learning.
■ Began as a collaboration between Nvidia and King’s College London
■ Prerna Dogra (Nvidia) and Jorge Cardoso (KCL)
■ Optimized for biomedical applications
■ Medical formats, medical images, transforms, loss functions, metrics
■ Strong emphasis on reproducibility
MONAI IS A GROWING COMMUNITY (Since April 2020)
Encapsulating a COVID-19 Algorithm into an Integrated AI Application
MONAI:End-End Training Workflow in 10 Lines of Code
from monai.application import MedNISTDataset
from monai.data import DataLoader
from monai.transforms import LoadPNGd, AddChanneld, ScaleIntensityd, ToTensord, Compose
from monai.networks.nets import densenet121
from monai.inferers import SimpleInferer
from monai.engines import SupervisedTrainer
transform = Compose(
dataset = MedNISTDataset(root_dir="./", transform=transform, section="training", download=True)
trainer = SupervisedTrainer(
train_data_loader=DataLoader(dataset, batch_size=2, shuffle=True, num_workers=4),
network=densenet121(spatial_dims=2, in_channels=1, out_channels=6),