# Contrastive learning for regression in multi-site brain age prediction

arXiv: https://arxiv.org/abs/2211.08326 · HTML: https://mlsanity.com/paper/2211.08326/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker](https://mlsanity.com/paper/1612.02572/) (2016)
- [Dissecting Supervised Contrastive Learning](https://mlsanity.com/paper/2102.08817/) (2021)
- [Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification](https://mlsanity.com/paper/2106.08808/) (2021)
- [Detect and Correct Bias in Multi-Site Neuroimaging Datasets](https://mlsanity.com/paper/2002.05049/) (2020)
- [Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects](https://mlsanity.com/paper/1910.04597/) (2019)
- [Unbiased Supervised Contrastive Learning](https://mlsanity.com/paper/2211.05568/) (2022)
- [Conditional Contrastive Learning with Kernel](https://mlsanity.com/paper/2202.05458/) (2022)

## Built on by (most cited first, 14 of 14)

- [ConR: Contrastive Regularizer for Deep Imbalanced Regression](https://mlsanity.com/paper/2309.06651/) (2023)
- [Anatomical Foundation Models for Brain MRIs](https://mlsanity.com/paper/2408.07079/) (2024)
- [Separating common from salient patterns with Contrastive Representation Learning](https://mlsanity.com/paper/2402.11928/) (2024)
- [SupReMix: Supervised Contrastive Learning for Medical Imaging Regression with Mixup](https://mlsanity.com/paper/2309.16633/) (2023)
- [Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps](https://mlsanity.com/paper/2408.00527/) (2024)
- [DualTalker: A Cross-Modal Dual Learning Approach for Speech-Driven 3D Facial Animation](https://mlsanity.com/paper/2311.04766/) (2023)
- [Weakly-supervised positional contrastive learning: application to cirrhosis classification](https://mlsanity.com/paper/2307.04617/) (2023)
- [SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions](https://mlsanity.com/paper/2406.00365/) (2024)
- [Robust brain age estimation from structural MRI with contrastive learning](https://mlsanity.com/paper/2507.01794/) (2025)
- [MRI-Based Brain Age Estimation with Supervised Contrastive Learning of Continuous Representation](https://mlsanity.com/paper/2511.22102/) (2025)
- [Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data](https://mlsanity.com/paper/2602.13846/) (2026)
- [A Unified Geometric Framework for Weighted Contrastive Learning](https://mlsanity.com/paper/2605.13943/) (2026)
- [M2LG-DG: A Multi-modal Local-Global Domain Generalization Framework for Cross-site Major Depressive Disorder Classification](https://mlsanity.com/paper/2609.09186/) (2026)
- [Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders](https://mlsanity.com/paper/2609.37642/) (2026)

## Abstract (arXiv)

Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and find new biomarkers. To estimate accurate and generalizable models, large datasets have been collected, which are often multi-site and multi-scanner. This large heterogeneity negatively affects the generalization performance of DL models since they are prone to overfit site-related noise. Recently, contrastive learning approaches have been shown to be more robust against noise in data or labels. For this reason, we propose a novel contrastive learning regression loss for robust brain age prediction using MRI scans. Our method achieves state-of-the-art performance on the OpenBHB challenge, yielding the best generalization capability and robustness to site-related noise.