Deep Learning for Healthcare Image Analysis

16th September 2018 from 15:00h until 19:00h

EVENT OVERVIEW

AI is reshaping the healthcare industry. GPU-accelerated deep learning solutions are used to design more sophisticated neural networks for healthcare and medical research applications, from pathology to clinical care to imaging.

This hands-on workshop explores the usage of Deep Learning in Medical Imaging.

Part 1 of the hands-on training:

Data Augmentation and Segmentation with Generative Networks for Medical Imaging

Generative Adversarial Networks (GANs) are pairs 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 medical deep learning, because it can augment smaller datasets for training of traditional networks. 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.

Part 2 of the hands-on training:

Coarse to Fine Contextual Memory for Medical Imaging

Coarse-to-Fine Context Memory (CFCM) is a technique developed for image segmentation using very deep architectures and incorporating features from many different scales with convolutional Long Short Term Memory (LSTM). In this session, you'll:

  • Take a deep dive into encoder-decoder architectures for medical image segmentation
  • Get to know common building blocks (convolutions, pooling layers, residual nets, etc.)
  • Investigate different strategies for skip connections
  • You must bring your own laptop to take this training.
  • Ensure your laptop will run smoothly by going to http://websocketstest.com/. 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.
  • Create an account under courses.nvidia.com/join.

    This event is only available to those attending the MICCAI 2018 the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, which will be held from September 16-20, 2018 in Granada, Spain.

    If you wish to contact us about this event, click here.

LOCATION

Granada Exhibition and Conference Centre, S/N, Paseo del Violón, 18006, Granada, Spain

INSTRUCTORS

NICOLA RIEKE

NVIDIA
Solution Architect

FAUSTO MILLETARI

NVIDIA
Senior Solutions Architect

DLI AT MICCAI 2018