Medical Image Reconstruction Using Generative Adversarial Network for Alzheimer Disease Assessment with Class-Imbalance Problem

Hu, Shengye; Yu, Wen; Chen, Zhuo; Wang, Shuqiang

doi:10.1109/iccc51575.2020.9344912

Cited by 64 publications

(22 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the adversarial training process of the GAN, the Generator generates fake images, and the Discriminator distinguishes real images from fake images until the Generator produces images that the Discriminator can no longer distinguish [49]. One study reconstructed plausible PET images from a gaussian noise distribution (2048dimensional noise), reporting MMD of 1.78 and SSIM of 0.53 [50]. Another study created synthetic PET images of patients in NC, MCI, and AD stages using deep convolutional GAN (DCGAN) [16].…”

Section: Protein Biomarkers For Admentioning

confidence: 99%

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Seo

Lee

2022

Life

View full text Add to dashboard Cite

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

show abstract

Section: Protein Biomarkers For Admentioning

confidence: 99%

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Seo

Lee

2022

Life

View full text Add to dashboard Cite

show abstract

“…However, to the best of our knowledge, no effort has been devoted to the development of point cloud reconstruction of brains. Since Deep learning technology has been popularized in the medical prediction [16,17,18], and has been applied in many fields such as maturity recognition [19,20], disease analysis [21,22,23,24,25], data generation [26,27], there are many works that combine deep learning with 3D data for accurate reconstruction [28,29,30]. Generative adversarial network, as well as many of its variants [31,32,33], is a widely used generative model and is known for its good generation quality.…”

Section: Introductionmentioning

confidence: 99%

A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction

Lei

Shen

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accuracy in brain surgery. However, compared to the substantial image data, it is almost impossible to obtain the intraoperative 3D shape information by using physical methods such as sensor scanning, especially in minimally invasive surgery and robot-guided surgery. In this paper, a general generative adversarial network (GAN) architecture based on graph convolutional networks is proposed to reconstruct the 3D point clouds (PCs) of brains by using one single 2D image, thus relieving the limitation of acquiring 3D shape data during surgery. Specifically, a tree-structured generative mechanism is constructed to use the latent vector effectively and transfer features between hidden layers accurately. With the proposed generative model, a spontaneous image-to-PC conversion is finished in real-time. Competitive qualitative and quantitative experimental results have been achieved on our model. In multiple evaluation methods, the proposed model outperforms another common point cloud generative model PointOutNet.

show abstract

“…The basic principle is variational inference [19,20,21] which maximizes the entropy of the probability distribution. It has been applied successfully in medical image analysis [22,23,24,25] and citation network [26,27]. Besides, Convolution Neural Network(CNN) has great power in recognizing disease-related images [28,29,30,32,33], which can be utilized to extract features of MRI in data space by a model pre-trained from a great many of unimodal images [31,34].…”

Section: Introductionmentioning

confidence: 99%

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

Zuo

Lei

Shen

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Multimodal neuroimage can provide complementary information about the dementia, but small size of complete multimodal data limits the ability in representation learning. Moreover, the data distribution inconsistency from different modalities may lead to ineffective fusion, which fails to sufficiently explore the intra-modal and intermodal interactions and compromises the disease diagnosis performance.To solve these problems, we proposed a novel multimodal representation learning and adversarial hypergraph fusion (MRL-AHF) framework for Alzheimer's disease diagnosis using complete trimodal images. First, adversarial strategy and pre-trained model are incorporated into the MRL to extract latent representations from multimodal data. Then two hypergraphs are constructed from the latent representations and the adversarial network based on graph convolution is employed to narrow the distribution difference of hyperedge features. Finally, the hyperedge-invariant features are fused for disease prediction by hyperedge convolution. Experiments on the public Alzheimer's Disease Neuroimaging Initiative(ADNI) database demonstrate that our model 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.

show abstract

Medical Image Reconstruction Using Generative Adversarial Network for Alzheimer Disease Assessment with Class-Imbalance Problem

Cited by 64 publications

References 10 publications

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction

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

Contact Info

Product

Resources

About