Difference between revisions of "2017 Winter Project Week/OCM-MRI"

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* Identified Generative Adversarial Networks (GAN) and Variational Autoencoders as possible approach
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* Discussions
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* Installation of and finding our way around Keras and TensorFlow
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* Network candidates: CNN with logistic regression output, Generative Adversarial Networks (GAN), Variational Autoencoders
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* In particular, Variational Autoencoders are able to model conditional distributions as required here
 
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Latest revision as of 15:41, 13 January 2017

Home < 2017 Winter Project Week < OCM-MRI

Key Investigators

  • Frank Preiswerk, Brigham and Women's Hospital, Harvard Medical School
  • Yaofei Wang (Vivian), Tianjin University, Beijing, China

Project Description

Objective Approach and Plan Progress and Next Steps
  • To investigate the use of Deep Learning for the generation of synthetic respiratory MR images.
  • Look into different software frameworks, choose one
  • Investigate generative deep neural network techniques
  • Produce preliminary results on hybrid US+MRI data
  • Discussions
  • Installation of and finding our way around Keras and TensorFlow
  • Network candidates: CNN with logistic regression output, Generative Adversarial Networks (GAN), Variational Autoencoders
  • In particular, Variational Autoencoders are able to model conditional distributions as required here

Background and References