Tensorizing GAN With High-Order Pooling for Alzheimer’s Disease Assessment

@article{Yu2022TensorizingGW,
  title={Tensorizing GAN With High-Order Pooling for Alzheimer’s Disease Assessment},
  author={Wen Yu and Baiying Lei and Michael K. Ng and Albert C. Cheung and Yanyan Shen and Shuqiang Wang},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2022},
  volume={33},
  pages={4945-4959}
}
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… 

Multiscale Autoencoder with Structural-Functional Attention Network for Alzheimer's Disease Prediction

A simple but highly end-to-end model, a multiscale autoencoder with structural-functional attention network (MASAN) to extract disease-related representations using T1-weighted Imaging (T1WI) and functional MRI (fMRI) and shows that there are higher weights on putative AD-related brain regions (such as the hippocampus, amygdala, etc.), and these regions are much more informative in anatomical studies.

Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease

By capturing the deep complementary information between structural features and functional features, the proposed CT-GAN can detect the AD-related brain connectivity, which could be used as a bio-marker of AD.

Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN

By introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently and clearly delineate the subtle lesions via MR image transformations between the source domain and the predefined target domain.

Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis

A novel Hypergraph Generative Adversarial Networks (HGGAN) is proposed in this paper, which utilizes Interactive Hyperedge Neurons module and Optimal Hypergraph Homomorphism algorithm to generate multimodal connectivity of Brain Network from rs-fMRI combination with DTI.

Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection

The result has shown that the use of the combination method would increase the accuracy of AD detection, and according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.

Adversarial Learning Based Structural Brain-network Generative Model for Analyzing Mild Cognitive Impairment

An adversarial learning-based structural brain-network generative model (SBGM) is proposed to directly learn the structural connections from brain diffusion tensor images and found that structural connectivity progressed in a progressively weaker direction as the condition worsened.

Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction

A novel multimodal representation learning and adversarial hypergraph fusion (MRL-AHF) framework for Alzheimer’s disease diagnosis using complete trimodal images achieves superior performance on Alzheimer's disease detection compared with other related models and provides a possible way to understand the underlying mechanisms of disorder's progression by analyzing the abnormal brain connections.

Multi-task Learning with Adaptive Global Temporal Structure for Predicting Alzheimer's Disease Progression

A novel penalty termed LSA is proposed to adaptively capture the intrinsic global temporal correlation among multiple time points and thus utilize the accumulated disease progression information.

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

The A3C-TL-GTO framework for MRI image classification and AD detection was an excellent instrument for this task, with a significant potential advantage for patient care and a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.

References

SHOWING 1-10 OF 50 REFERENCES

Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities to aid the diagnosis of AD and has the potential to require less labeled data.

Classification of MR brain images by combination of multi-CNNs for AD diagnosis

This paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis.

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification and achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for Classification pMCI vs.NC, demonstrating the promising classification performance.

Exploiting Discriminative Regions of Brain Slices Based on 2D CNNs for Alzheimer’s Disease Classification

This paper aims to construct novel AD classification models which have a good performance and interpretation on AD diagnosis, and proposes the three classifiers including a simple broaden plain CNNs, a major slice-assemble CNNs and a multi-slice CNNs which record the slice positions but have fewer parameters.

Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer's Disease Diagnosis

Experimental results on subjects from ADNI demonstrate that the authors' synthesized PET images with 3D-cGAN are reasonable, and also the two-stage deep learning method outperforms the state-of-the-art methods in AD diagnosis.

Automatic Recognition of Mild Cognitive Impairment and Alzheimers Disease Using Ensemble based 3D Densely Connected Convolutional Networks

An ensemble of 3D densely connected convolutional networks (3D-DenseNets) for AD and MCI diagnosis with weighted-based fusion method and superior performance was demonstrated on ADNI dataset including 833 subjects.

A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

The proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space, the removal of the normal aging effect, selection of discriminative voxels, and the integration of sparse representation technique and the combination of cognitive measures.

Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks

A deep convolutional neural network can identify different stages of Alzheimer’s disease and obtains superior performance for early-stage diagnosis and outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.