ArticleIEEE transactions on medical imaging2022
Deep Relation Learning for Regression and Its Application to Brain Age Estimation.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Infant Brain Age Estimation With T1w/T2w Ratio MRI: A Myelination-Aware Deep Learning Approach.Journal of magnetic resonance imaging : JMRI · 2026Article
- Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.Brain informatics · 2026Article
- Hybrid deep learning model for brain age prediction using time-distributed convolutional and bidirectional LSTM networks.Scientific reports · 2026Article
- superLPNet: a super lightweight parameter deep learning model for brain age estimation from structural MRI.Magma (New York, N.Y.) · 2026Article
- [Research progress on intelligent brain age prediction methods in diagnosis of Parkinson's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Machine learning to infer neurocognitive testing scores among adolescents and young adults with congenital heart disease.Communications medicine · 2026Article
- MDFNet: a multi-dimensional feature fusion model based on structural magnetic resonance imaging representations for brain age estimation.Magma (New York, N.Y.) · 2026Article
- Mammo-AGE: deep learning estimation of breast age from mammograms.Nature communications · 2025Article
- Brain age gap as a predictive biomarker that links aging, lifestyle, and neuropsychiatric health.Communications medicine · 2025Article
- Development and Validation of a Brain Aging Biomarker in Middle-Aged and Older Adults: Deep Learning Approach.JMIR aging · 2025Article
- A review of artificial intelligence-based brain age estimation and its applications for related diseases.Briefings in functional genomics · 2025Review
- BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): I. MRI and Lesion Labeling.Scientific data · 2025Article
- Deep learning of structural MRI predicts fluid, crystallized, and general intelligence.Scientific reports · 2024Article
- Analysis of Brain Age Gap across Subject Cohorts and Prediction Model Architectures.Biomedicines · 2024Article
- Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection.Brain informatics · 2024Article
- A ResNet mini architecture for brain age prediction.Scientific reports · 2024Article
- Predicting the age of fieldScience advances · 2024Article
- Regional choroidal thickness estimation from color fundus images based on convolutional neural networks.Heliyon · 2024Article
- Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age.Frontiers in aging neuroscience · 2023Article
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4 authors.
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Abstract
Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: "cumulative relation," "relative relation," "maximal relation" and "minimal relation." These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side).
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