Morphological Feature Visualization of Alzheimer's Disease via Multidirectional Perception GAN

Wen Yu, Baiying Lei, Shuqiang Wang*, Yong Liu, Zhiguang Feng, Yong Hu, Yanyan Shen*, Michael K. Ng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

44 Citations (Scopus)

Abstract

The diagnosis of early stages of Alzheimer's disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features for early stages of AD is of great clinical value. In this work, a novel multidirectional perception generative adversarial network (MP-GAN) is proposed to visualize the morphological features indicating the severity of AD for patients of different stages. Specifically, by introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently. Thus, using the class discriminative map from the generator, the proposed model can clearly delineate the subtle lesions via MR image transformations between the source domain and the predefined target domain. Besides, by integrating the adversarial loss, classification loss, cycle consistency loss, and L1 penalty, a single generator in MP-GAN can learn the class discriminative maps for multiple classes. Extensive experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that MP-GAN achieves superior performance compared with the existing methods. The lesions visualized by MP-GAN are also consistent with what clinicians observe.

Original languageEnglish
Pages (from-to)4401-4415
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume34
Issue number8
Early online date23 Mar 2022
DOIs
Publication statusPublished - Aug 2023

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Alzheimer's disease (AD)
  • generative adversarial networks (GANs)
  • lesion visualization
  • magnetic resonance (MR) images

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