Conditional recurrent flow: Conditional generation of longitudinal samples with applications to neuroimaging

Seong Jae Hwang, Zirui Tao, Vikas Singh, Won Hwa Kim

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

3 Citations (Scopus)


We develop a conditional generative model for longitudinal image datasets based on sequential invertible neural networks. Longitudinal image acquisitions are common in various scientific and biomedical studies where often each image sequence sample may also come together with various secondary (fixed or temporally dependent) measurements. The key goal is not only to estimate the parameters of a deep generative model for the given longitudinal data, but also to enable evaluation of how the temporal course of the generated longitudinal samples are influenced as a function of induced changes in the (secondary) temporal measurements (or events). Our proposed formulation incorporates recurrent subnetworks and temporal context gating, which provides a smooth transition in a temporal sequence of generated data that can be easily informed or modulated by secondary temporal conditioning variables. We show that the formulation works well despite the smaller sample sizes common in these applications. Our model is validated on two video datasets and a longitudinal Alzheimer's disease (AD) dataset for both quantitative and qualitative evaluations of the generated samples. Further, using our generated longitudinal image samples, we show that we can capture the pathological progressions in the brain that turn out to be consistent with the existing literature, and could facilitate various types of downstream statistical analysis.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Computer Vision, ICCV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728148038
Publication statusPublished - 2019 Oct
Event17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
Duration: 2019 Oct 272019 Nov 2

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Country/TerritoryKorea, Republic of

Bibliographical note

Funding Information:
Research supported by NIH (R01AG040396, R01EB022883, R01AG062336, R01AG059312), UW CPCP (U54AI117924), UW CIBM (T15LM007359) NSF CAREER Award (1252725), USDOT Research and Innovative Technology Administration (69A3551747134), and UTA Research Enhancement Program (REP).

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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