# Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment

arXiv: https://arxiv.org/abs/2008.00748 · HTML: https://mlsanity.com/paper/2008.00748/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 21 of 21)

- [Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction](https://mlsanity.com/paper/2107.09928/) (2021)
- [Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis](https://mlsanity.com/paper/2107.09953/) (2021)
- [Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN](https://mlsanity.com/paper/2111.12886/) (2021)
- [DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease](https://mlsanity.com/paper/2110.05712/) (2021)
- [Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience](https://mlsanity.com/paper/2206.07081/) (2022)
- [3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image](https://mlsanity.com/paper/2107.11010/) (2021)
- [Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2302.08641/) (2023)
- [A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction](https://mlsanity.com/paper/2107.09923/) (2021)
- [Adversarial Learning Based Structural Brain-network Generative Model for Analyzing Mild Cognitive Impairment](https://mlsanity.com/paper/2208.08896/) (2022)
- [Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI](https://mlsanity.com/paper/2305.14404/) (2023)
- [Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network](https://mlsanity.com/paper/2309.16206/) (2023)
- [A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease](https://mlsanity.com/paper/2110.09302/) (2021)
- [Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease](https://mlsanity.com/paper/2206.13393/) (2022)
- [MHNet: Multi-view High-order Network for Diagnosing Neurodevelopmental Disorders Using Resting-state fMRI](https://mlsanity.com/paper/2407.03217/) (2024)
- [Feature-selected Graph Spatial Attention Network for Addictive Brain-Networks Identification](https://mlsanity.com/paper/2207.00583/) (2022)
- [Dynamic Community Detection via Adversarial Temporal Graph Representation Learning](https://mlsanity.com/paper/2207.03580/) (2022)
- [SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image](https://mlsanity.com/paper/2305.12646/) (2023)
- [Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI](https://mlsanity.com/paper/2306.09629/) (2023)
- [BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease](https://mlsanity.com/paper/2309.08916/) (2023)
- [Classification of developmental and brain disorders via graph convolutional aggregation](https://mlsanity.com/paper/2311.07370/) (2023)
- [Quantum-inspired tensor networks in machine learning models](https://mlsanity.com/paper/2604.14287/) (2026)

## Abstract (arXiv)

It is of great significance to apply deep learning for the early diagnosis of Alzheimer's Disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to assess Mild Cognitive Impairment (MCI) and AD. By tensorizing a three-player cooperative game based framework, the proposed model can benefit from the structural information of the brain. By incorporating the high-order pooling scheme into the classifier, the proposed model can make full use of the second-order statistics of the holistic Magnetic Resonance Imaging (MRI) images. To the best of our knowledge, the proposed Tensor-train, High-pooling and Semi-supervised learning based GAN (THS-GAN) is the first work to deal with classification on MRI images for AD diagnosis. Extensive experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset are reported to demonstrate that the proposed THS-GAN achieves superior performance compared with existing methods, and to show that both tensor-train and high-order pooling can enhance classification performance. The visualization of generated samples also shows that the proposed model can generate plausible samples for semi-supervised learning purpose.