Deep Geometrical Learning for Alzheimer's Disease Progression Modeling

Seungwoo Jeong, Wonsik Jung, Junghyo Sohn, Heung Il Suk

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

Abstract

Alzheimer's disease (AD) is widely aware as a neurodegenerative disease that is characterized as a leading cause of irreversible progressive dementia. From a clinical perspective, it is vital to forecast a patient's progression over time. In that regard, there have been rigorous researches on AD progression modeling with structural magnetic resonance imaging (MRI). Methodologically, there are three major aspects of MRI modeling: (i) variability over time, (ii) sparseness in observations, and (iii) geometrical properties in temporal dynamics. While the existing deep-learning-based methods have addressed variability or sparsity in data, there is still a need to take into account the inherent geometrical properties. Recently, geometric modeling based on ordinary differential equations (ODE-RGRU) has shown its ability in various time-series data by combining an RNN and an ordinary differential equation (ODE) in symmetric positive definite (SPD) space. Despite the success of ODE-RGRU in time-series data modeling, it is limited to estimating the SPD matrix from sparse data with missing values. To this end, we propose a novel geometric learning framework for AD progression modeling to tackle the aforementioned issues simultaneously. And, we also propose training algorithms for manifold mapping on irregular and incomplete MRI and cognitive scores observations. Our proposed framework efficiently learns three major aspects of longitudinal MRI biomarker and cognitive scores by the manifold transformation module, ODE-RGRU, and missing value estimation module. We demonstrate the effectiveness of our method in experiments that forecast multi-class classification and cognitive scores over time. Additionally, we provide a multi-faceted analysis of the proposed method through an ablation study.

Original languageEnglish
Title of host publicationProceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022
EditorsXingquan Zhu, Sanjay Ranka, My T. Thai, Takashi Washio, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages211-220
Number of pages10
ISBN (Electronic)9781665450997
DOIs
Publication statusPublished - 2022
Event22nd IEEE International Conference on Data Mining, ICDM 2022 - Orlando, United States
Duration: 2022 Nov 282022 Dec 1

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2022-November
ISSN (Print)1550-4786

Conference

Conference22nd IEEE International Conference on Data Mining, ICDM 2022
Country/TerritoryUnited States
CityOrlando
Period22/11/2822/12/1

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2019-0-00079 , Artificial Intelligence Graduate School Program(Korea University) and No. 2022-0-00959, (Part 2) Few-ShotLearning of Causal Inference in Vision and Language for Decision Making).

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Alzheimer's disease progression
  • deep learning
  • geometric modeling
  • neural ordinary differential equations

ASJC Scopus subject areas

  • General Engineering

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