Search Results for author:
Found 18 papers, 5 papers with code
Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI
To address these issues, we propose a Joint image Denoising and motion Artifact Correction (JDAC) framework via iterative learning to handle noisy MRIs with motion artifacts, consisting of an adaptive denoising model and an anti-artifact model.
Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework
The SST utilizes an energy-based model to comprehend the global latent distribution of a target domain and translate source latent codes toward the target domain, while SMS enables MRI synthesis with a target-specific style.
Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis
The model pretrained on large-scale rs-fMRI data has been released to the public.
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification
In the personalized branch, we integrate vectorized demographic information (i. e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics.
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI
Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in brain and is widely used for brain disorder analysis. Previous studies propose to extract fMRI representations through diverse machine/deep learning methods for subsequent analysis.
Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI
Besides the encoder, the pretext model also contains two decoders for two auxiliary tasks (i. e., MRI reconstruction and brain tissue segmentation), while the downstream model relies on a predictor for classification.
Federated Learning for Medical Image Analysis: A Survey
In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches.
Source-Free Unsupervised Domain Adaptation: A Survey
Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden.
Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI
In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data.
DomainATM: Domain Adaptation Toolbox for Medical Data Analysis
To this end, we have developed the Domain Adaptation Toolbox for Medical data analysis (DomainATM) - an open-source software package designed for fast facilitation and easy customization of domain adaptation methods for medical data analysis.
Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline with Structural MRI
Meanwhile, the brain disease related regions can be highlighted by the attention mechanism.
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack
Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images.
Domain Adaptation for Medical Image Analysis: A Survey
The aim of this paper is to survey the recent advances of domain adaptation methods in medical image analysis.
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders.
Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images
Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice).
Geometric Interpretation of Running Nyström-Based Kernel Machines and Error Analysis
These analytical results lead to the conjecture that the naive approach can provide more accurate approximate solutions than the other two sophisticated approaches.
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance.
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains.
