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

NICOLA RIEKE

NVIDIA
Solution Architect

Fausto Milletari

FAUSTO MILLETARI

NVIDIA
Senior Solutions Architect

DLI AT MICCAI 2018

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NICOLA RIEKE

NICOLA RIEKE


Solution Architect
NVIDIA 

Nicola Rieke is a solution architect at NVIDIA for deep learning in healthcare with several years of experience in the intersection of mathematics, medicine and computer science. With a broad expertise in the field of medical image processing, computer-aided medical procedures and applied machine learning, her primary responsibility is to support the medical imaging community in advancing deep learning solutions. She published various peer reviewed papers, in particular on real-time machine learning approaches for computer assistance in surgical interventions, and was honored with the prestigious MICCAI Young Scientist Award during her doctoral study at the Technical University of Munich. 

 

FAUSTO MILLETARI

FAUSTO MILLETARI


Senior Solutions Architect
NVIDIA 

Fausto Milletari is a Senior Solutions Architect at NVIDIA and a Ph.D. graduate from the Technical University of Munich. Fausto's major research topic is the segmentation of medical images. He has pioneered the usage of 3D convolutional neural networks to segment MRI and Ultrasound volumetric data and other 3D deep-learning techniques. In addition, he works on a variety of other computer vision problems, such as object tracking and detection. His work focuses on pattern recognition and machine learning, and in particular on deep learning techniques. Several of his contributions have been presented in recent editions of MICCAI, IPCAI, BMVC, 3DV and in specialized journals. His current job at NVIDIA focuses on the application of artificial intelligence techniques in medicine at the boundary between research and commercial applications.

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