MaIIN

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Disentangled Representation Learning of Medical Brain Images using Flow-based Models

Supervision: Prof. Dr. Nassir Navab, Dr. Seong Tae Kim, Matthias Keicher

Abstract

Generative Models like GANs and VAEs don't learn the data distribution directly as the distribution tends to be intractable. Instead, these models approximate a lower bound on the log-likelihood of the data (VAEs) or use an adversarial network to train the generator(GANs). Invertible flow-based models instead directly optimize for the log-likelihood of the data using normalizing flows. In this project, we study the use of flow-based models in learning meaningful, disentangled representations of medical brain images in both supervised and unsupervised settings. Flow-based models also learn meaningful latent representation which can be used for downstream tasks like meaningful image manipulation. We expect disentangled representations would allow for control over the generative factors of the images, which could be used to generate highly controlled synthetic images for training other models that require a large number of labeled or unlabelled data.

Requirements:

  • Good understanding of statistics and machine learning methods.
  • Very good programming skills in Python & TensorFlow? / PyTorch?

Location:

  • Garching

ProjectForm
Title: Disentangled Representation Learning of Medical Brain Images using Flow-based Models
Abstract: Generative Models like GANs and VAEs don't learn the data distribution directly as the distribution tends to be intractable. Instead, these models approximate a lower bound on the log-likelihood of the data (VAEs) or use an adversarial network to train the generator(GANs). Invertible flow-based models instead directly optimize for the log-likelihood of the data using normalizing flows. In this project, we study the use of flow-based models in learning meaningful, disentangled representations of medical brain images in both supervised and unsupervised settings. Flow-based models also learn meaningful latent representation which can be used for downstream tasks like meaningful image manipulation. We expect disentangled representations would allow for control over the generative factors of the images, which could be used to generate highly controlled synthetic images for training other models that require a large number of labeled or unlabelled data.
Student: Aadhithya Sankar
Director: Prof. Dr. Nassir Navab
Supervisor: Dr. Seong Tae Kim, Matthias Keicher
Type: Master Thesis
Area: Machine Learning, Medical Imaging
Status: running
Start:  
Finish:  
Thesis (optional):  
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Edit | Attach | Refresh | Diffs | More | Revision r1.1 - 23 Sep 2020 - 10:15 - SeongTaeKim