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Found 31 papers, 3 papers with code
Brain Diffuser with Hierarchical Transformer for MCI Causality Analysis
It can captures both unidirectal and bidirectional interactions between brain regions, providing a comprehensive understanding of the brain's information processing mechanisms.
BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model
In light of this, we introduce a novel method called BrainNetDiff, which combines a multi-head Transformer encoder to extract relevant features from fMRI time series and integrates a conditional latent diffusion model for brain network generation.
MedPrompt: Cross-Modal Prompting for Multi-Task Medical Image Translation
Within this framework, we introduce the Prompt Extraction Block and the Prompt Fusion Block to efficiently encode the cross-modal prompt.
DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI
In this paper, a novel diffusision generative adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict SC from brain fMRI in an end-to-end manner.
Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network
The CT-GAN can learn topological features and generate multimodal connectivity from multimodal imaging data in an efficient end-to-end manner.
BGGAN: Generative AI Enables Representing Brain Structure-Function Connections for Alzheimer's Disease
Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BGGAN can employ features of direct and indirect brain regions to learn the mapping function between structural domain and functional domain.
Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer With Adaptive Channel Expansion
Vignetting commonly occurs as a degradation in images resulting from factors such as lens design, improper lens hood usage, and limitations in camera sensors.
DocDeshadower: Frequency-aware Transformer for Document Shadow Removal
In this study, we propose the DocDeshadower, a multi-frequency Transformer-based model built on Laplacian Pyramid.
Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI
Integrating the brain structural and functional connectivity features is of great significance in both exploring brain science and analyzing cognitive impairment clinically.
SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image
The Stage-II GAN takes the results from Stage-I and generates high-density point clouds with detailed features.
Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline
Brain network analysis is essential for diagnosing and intervention for Alzheimer's disease (AD).
A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement
In order to facilitate film-based image stylization research, we construct FilmSet, a large-scale and high-quality film style dataset.
WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement
Image enhancement is a technique that frequently utilized in digital image processing.
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits
And models of functional addiction circuits developed from functional imaging are an effective tool for discovering and verifying addiction circuits.
ShaDocNet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal
Shadow removal improves the visual quality and legibility of digital copies of documents.
Adversarial Learning Based Structural Brain-network Generative Model for Analyzing Mild Cognitive Impairment
Mild cognitive impairment(MCI) is a precursor of Alzheimer's disease(AD), and the detection of MCI is of great clinical significance.
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning
Dynamic community detection has been prospered as a powerful tool for quantifying changes in dynamic brain network connectivity patterns by identifying strongly connected sets of nodes.
Feature-selected Graph Spatial Attention Network for Addictive Brain-Networks Identification
Functional alterations in the relevant neural circuits occur from drug addiction over a certain period.
Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease
However, most existing methods applied in neuroimaging can not efficiently fuse the functional and structural information from multi-modal neuroimages.
Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN
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.
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease
The proposed model can evaluate characteristics of abnormal brain connections at different stages of Alzheimer's disease, which is helpful for cognitive disease study and early treatment.
DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease
In this work, a novel decoupling generative adversarial network (DecGAN) is proposed to detect abnormal neural circuits for AD.
3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image
A branching predictor and several hierarchical attention pipelines are constructed to generate point clouds that accurately describe the incomplete images and then complete these point clouds with high quality.
A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction
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.
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction
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.
Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis
Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis.
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
In this paper, we study the statistical properties of distributed kernel ridge regression together with random features (DKRR-RF), and obtain optimal generalization bounds under the basic setting, which can substantially relax the restriction on the number of local machines in the existing state-of-art bounds.
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain
Specifically, FP-GANs firstly divides an MR image into low-frequency global approximation and high-frequency anatomical texture in wavelet domain.
Brain Stroke Lesion Segmentation Using Consistent Perception Generative Adversarial Network
The assistant network and the discriminator are employed to jointly decide whether the segmentation results are real or fake.
Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis
Fusing multi-modality medical images, such as MR and PET, can provide various anatomical or functional information about human body.
Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment
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.
