Computer Vision / Video Analytics

Upcoming Webinar: Learn How to Use NVIDIA NGC Jupyter Notebook for Medical Imaging

AI-Generated Summary

  • NVIDIA NGC and NVIDIA Clara accelerate medical imaging workflows through GPU-optimized containers and pretrained models.
  • A sample Jupyter notebook with a pretrained 3D image segmentation model is available from the NGC catalog.
  • The webinar demonstrates how to retrain the model using custom hyperparameters and test it with custom checkpoints.
  • Next steps include exploring MONAI and NVIDIA Clara Imaging for further development.

Next Step

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The NVIDIA NGC team is hosting a webinar with live Q&A to dive into our new Jupyter notebook available from the NGC catalog. Learn how to use these resources to kickstart your AI journey.

NVIDIA NGC Jupyter Notebook Day: Building a 3D Medical Imaging Segmentation Model
Thursday, July 22 at 9:00 AM PT

Image segmentation deals with placing each pixel (or voxel in the case of 3D) of an image into specific classes that share common characteristics. In medical imaging, image segmentation can be used to help identify organs and anomalies, measure them, classify them, and even uncover diagnostic information by using data gathered from x-rays, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and more. However, building, training, and optimizing an accurate image segmentation AI model from scratch can be time consuming for novices and experts alike.

By joining this webinar, you will learn:

  • How NVIDIA NGC and NVIDIA Clara (NVIDIA’s healthcare AI Platform) can help accelerate medical imaging workflows
  • How to use a sample Jupyter notebook with a pretrained image segmentation model from the NGC catalog
  • How to refine the model by retraining it using your own hyperparameters and test it using your own checkpoints
  • Where to go for next steps, including MONAI and NVIDIA Clara Imaging

Register now >>

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