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MONAI Medical Image Deep Learning: A 3-Minute Introduction

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MONAI Medical Image Deep Learning: A 3-Minute Introduction

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These slides are meant as a "teaser", to get people interested in learning more about MONAI. They provide a brief summary of the motivation for and the potential of MONAI.

These were presented at the start of the 2020 3D Slicer Project Week. The recording of that presentation is available online:
https://www.youtube.com/watch?v=tBrMVTlzb8s

These slides are meant as a "teaser", to get people interested in learning more about MONAI. They provide a brief summary of the motivation for and the potential of MONAI.

These were presented at the start of the 2020 3D Slicer Project Week. The recording of that presentation is available online:
https://www.youtube.com/watch?v=tBrMVTlzb8s

More Related Content

MONAI Medical Image Deep Learning: A 3-Minute Introduction

  1. 1. 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
  2. 2. 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
  3. 3. MONAI IS A GROWING COMMUNITY (Since April 2020) 41
  4. 4. Encapsulating a COVID-19 Algorithm into an Integrated AI Application Nvidia CLARA
  5. 5. 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( [ LoadPNGd(keys="image"), AddChanneld(keys="image"), ScaleIntensityd(keys="image"), ToTensord(keys=["image", "label"]) ] ) dataset = MedNISTDataset(root_dir="./", transform=transform, section="training", download=True) trainer = SupervisedTrainer( max_epochs=5, train_data_loader=DataLoader(dataset, batch_size=2, shuffle=True, num_workers=4), network=densenet121(spatial_dims=2, in_channels=1, out_channels=6), optimizer=torch.optim.Adam(model.parameters(),lr=1e-5), loss_function=torch.nn.CrossEntropyLoss(), inferer=SimpleInferer() ) trainer.run()

Editor's Notes

  • 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.

  • ×