ArticleMedical image analysis2021
Multi-site MRI harmonization via attention-guided deep domain adaptation for brain disorder identification.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 135 citations in OpenAlex.
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- Harmonization in magnetic resonance imaging: A survey of acquisition, image-level, and feature-level methods.Medical image analysis · 2026Review
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- A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.Neural networks : the official journal of the International Neural Network Society · 2026Article
- Adaptive multi-stage domain unlearning for white-matter lesion segmentation.Frontiers in medicine · 2026Article
- Evolution of deep learning for structural magnetic resonance imaging analysis in Alzheimer's disease: from slice-level classification to brain structure modeling.Frontiers in human neuroscience · 2026Review
- Toward neuroimaging-based diagnostic support: a deep learning approach with a closed-loop system for psychiatric classification.iScience · 2025Article
- MMDD: A Multimodal Multitask Dynamic Disentanglement Framework for Robust Major Depressive Disorder Diagnosis Across Neuroimaging Sites.Diagnostics (Basel, Switzerland) · 2025Article
- Differential dementia detection from multimodal brain images in a real-world dataset.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Prediction of Alzheimer's Disease Based on Multi-Modal Domain Adaptation.Brain sciences · 2025Article
- An evaluation of image-based and statistical techniques for harmonizing brain volume measurements.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Deep learning for the harmonization of structural MRI scans: a survey.Biomedical engineering online · 2024Review
- Performance changes due to differences among annotating radiologists for training data in computerized lesion detection.International journal of computer assisted radiology and surgery · 2024Article
- Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks.IEEE transactions on bio-medical engineering · 2024Article
- Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI.Neuroimage. Reports · 2024Article
- Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.Radiology · 2024Review
- Superpixel-ComBat modeling: A joint approach for harmonization and characterization of inter-scanner variability in T1-weighted images.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Article
- Structural MRI Harmonization via Disentangled Latent Energy-Based Style Translation.Machine learning in medical imaging. MLMI (Workshop) · 2023Article
- Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection.Human brain mapping · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
Structural magnetic resonance imaging (MRI) has shown great clinical and practical values in computer-aided brain disorder identification. Multi-site MRI data increase sample size and statistical power, but are susceptible to inter-site heterogeneity caused by different scanners, scanning protocols, and subject cohorts. Multi-site MRI harmonization (MMH) helps alleviate the inter-site difference for subsequent analysis. Some MMH methods performed at imaging level or feature extraction level are concise but lack robustness and flexibility to some extent. Even though several machine/deep learning-based methods have been proposed for MMH, some of them require a portion of labeled data in the to-be-analyzed target domain or ignore the potential contributions of different brain regions to the identification of brain disorders. In this work, we propose an attention-guided deep domain adaptation (AD
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.