Semi-supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control

Mahmoud Mostapha, Juan Prieto, Veronica Murphy, Jessica Girault, Mark Foster, Ashley Rumple, Joseph Blocher, Weili Lin, Jed Elison, John Gilmore, Steve Pizer, Martin Styner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Discriminative deep learning models have shown remarkable success in many medical image analysis applications. However, their success is limited in problems that involve learning from imbalanced and heterogeneous datasets. Generative models parameterized using deep learning models can resolve this problem by characterizing the distribution of well-represented classes, a step enabling the identification of samples that were improbably generated from that distribution. This paper proposes a semi-supervised out-of-sample detection framework based on a 3D variational autoencoder-based generative adversarial network (VAE-GAN). The proposed framework relies on a high-level similarity metric and invariant representations learned by a semi-supervised discriminator to evaluate the generated images. The encoded latent representations were constrained according to user-defined properties through a jointly trained predictor network. Anomaly samples are detected using learned similarity scores and/or scores from an online one-class neural network. The high performance of the proposed methods is confirmed via a novel application to the automatic quality control of structural MR images.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages127-136
Number of pages10
ISBN (Print)9783030322472
DOIs
StatePublished - Jan 1 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/17/19

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Keywords

  • Anomaly detection
  • GAN
  • MRI
  • Quality control
  • VAE

Cite this

Mostapha, M., Prieto, J., Murphy, V., Girault, J., Foster, M., Rumple, A., Blocher, J., Lin, W., Elison, J., Gilmore, J., Pizer, S., & Styner, M. (2019). Semi-supervised VAE-GAN for Out-of-Sample Detection Applied to MRI Quality Control. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 127-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS). Springer. https://doi.org/10.1007/978-3-030-32248-9_15