# Anatomical Foundation Models for Brain MRIs

arXiv: https://arxiv.org/abs/2408.07079 · HTML: https://mlsanity.com/paper/2408.07079/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation](https://mlsanity.com/paper/1904.07773/) (2019)
- [Rank-N-Contrast: Learning Continuous Representations for Regression](https://mlsanity.com/paper/2210.01189/) (2022)
- [Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification](https://mlsanity.com/paper/2106.08808/) (2021)
- [Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data](https://mlsanity.com/paper/2206.11417/) (2022)
- [Explainable Brain Age Prediction using coVariance Neural Networks](https://mlsanity.com/paper/2305.18370/) (2023)
- [Contrastive learning for regression in multi-site brain age prediction](https://mlsanity.com/paper/2211.08326/) (2022)
- [Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networks](https://mlsanity.com/paper/2402.07684/) (2024)
- [Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps](https://mlsanity.com/paper/2408.00527/) (2024)

## Built on by (most cited first, 13 of 13)

- [Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery](https://mlsanity.com/paper/2503.00580/) (2025)
- [Brain Foundation Models with Hypergraph Dynamic Adapter for Brain Disease Analysis](https://mlsanity.com/paper/2505.00627/) (2025)
- [Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease](https://mlsanity.com/paper/2507.20872/) (2025)
- [Bridging Brain with Foundation Models through Self-Supervised Learning](https://mlsanity.com/paper/2506.16009/) (2025)
- [GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models](https://mlsanity.com/paper/2605.01829/) (2026)
- [Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation](https://mlsanity.com/paper/2607.07219/) (2026)
- [Robust brain age estimation from structural MRI with contrastive learning](https://mlsanity.com/paper/2507.01794/) (2025)
- [Diffusion MRI Transformer with a Diffusion Space Rotary Positional Embedding (D-RoPE)](https://mlsanity.com/paper/2603.25977/) (2026)
- [Bayesian meta-learning for modeling Alzheimer's disease progression](https://mlsanity.com/paper/2606.02228/) (2026)
- [How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Models](https://mlsanity.com/paper/2606.08164/) (2026)
- [Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures](https://mlsanity.com/paper/2609.05438/) (2026)
- [A4-MR: Abnormality-aware Anatomical Alignment for MR](https://mlsanity.com/paper/2610.13056/) (2026)
- [Quantifying privacy risks in foundation model representations of brain MRI](https://mlsanity.com/paper/2610.20494/) (2026)

## Abstract (arXiv)

Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the most predominant biomarkers in neuroimaging is represented by brain age, which has been shown to be a good indicator for different conditions, such as Alzheimer's Disease. Using brain age for weakly supervised pre-training of DL models in transfer learning settings has also recently shown promising results, especially when dealing with data scarcity of different conditions. On the other hand, anatomical information of brain MRIs (e.g. cortical thickness) can provide important information for learning good representations that can be transferred to many downstream tasks. In this work, we propose AnatCL, an anatomical foundation model for brain MRIs that i.) leverages anatomical information in a weakly contrastive learning approach, and ii.) achieves state-of-the-art performances across many different downstream tasks. To validate our approach we consider 12 different downstream tasks for the diagnosis of different conditions such as Alzheimer's Disease, autism spectrum disorder, and schizophrenia. Furthermore, we also target the prediction of 10 different clinical assessment scores using structural MRI data. Our findings show that incorporating anatomical information during pre-training leads to more robust and generalizable representations. Pre-trained models can be found at: https://github.com/EIDOSLAB/AnatCL.