Anatomical Foundation Models for Brain MRIs

TL;DR

This paper introduces AnatCL, a novel anatomical foundation model for brain MRIs that uses weakly contrastive learning to incorporate anatomical information, achieving state-of-the-art results across multiple neuroimaging tasks.

Contribution

The work presents a new anatomical foundation model leveraging weakly contrastive learning, improving transfer learning performance in neuroimaging applications.

Findings

Incorporating anatomical info improves model robustness.

Pre-trained models outperform previous methods.

Effective across diverse neuroimaging tasks.

Abstract

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.