Data Augmentation and Segmentation with Generative Networks for Medical Imaging
09:00-12:00 January 26

½ day
Intermediate level

@ 2BC


A generative adversarial network (GAN) is a pair of deep neural networks: a generator that creates new examples based on the training data provided and a discriminator that attempts to distinguish between genuine and simulated data. As both networks improve together, the examples created become increasingly realistic. This technology is promising for healthcare, because it can augment smaller datasets for training of traditional networks. At this hands-on workshop you'll learn to:

  • Generate synthetic brain MRIs
  • Apply GANs for segmentation
  • Use GANs for data augmentation to improve accuracy


Upon completion, you'll be able to apply GANs to medical imaging use cases.


Experience with CNNs

Follow these steps prior to joining the training:

  • You must bring your own laptop in order to run the training.
  • A current browser is needed. For optimal performance, Chrome, Firefox or Safari for Macs are recommended. IE is operational but does not provide the best performance.
  • Create an account at
  • Ensure your laptop will run smoothly by going to
  • Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80).
  • If there are issues with WebSockets, try updating your browser.


Cristiana Dinea

Master Instructor, NVIDIA Deep Learning Institute

Nicola Rieke

Senior Deep Learning Solution Architect – Healthcare, NVIDIA


Deep Learning Institute