BrainIAC: A foundation model for generalized Brain MRI analysis

Overview

BrainIAC (Brain Imaging Adaptive Core) is vision based foundation model for generalized structural Brain MRI analysis. This repository provides the BrainIAC and downstream model checkpoints, with training/inference pipeline across all downstream tasks. Checkout the Paper

Installation

Prerequisites

• Python 3.9+
• CUDA 11.0+

Setup Environment

# Clone the repository
git clone https://github.com/YourUsername/BrainIAC_V2.git
cd BrainIAC

# Create conda environment

# Create conda environment
conda create -n brainiac python=3.9
conda activate brainiac
pip install -r requirements.txt

Model Checkpoints

Download the BrainIAC weights and downstream model checkpoints and place them in ./src/checkpoints/:

Quick Start

See quickstart.ipynb to get started on how to preprocess data, load BrainIAC to extract features, generate and visualize saliency maps. We provide data samples from publicly available UPENN-GBM License (with no modifications to the provided preprocessed images) and the Pixar License dataset in the sample_data directory.

Train and Infer Downstream Models

• Brain Age Prediction
• IDH Mutation Classification
• Mild Cognitive Impairment Classification
• Diffuse Glioma Overall Survival Prediction
• MR Sequence Classification
• Time to Stroke Prediction
• Tumor Segmentation

Brainiac Platform

BrainIAC and all the downstream models are hosted at Brainiac Platform for inference. The platform provides an interface for uploading the structural brain MRI data and running inference on the models including BrainIAC, brain age, MCI classification, time since stroke prediction etc.

Citation

@article{tak2026generalizable,
  title={A generalizable foundation model for analysis of human brain MRI},
  author={Tak, Divyanshu and Gormosa, B.A. and Zapaishchykova, A. and others},
  journal={Nature Neuroscience},
  year={2026},
  publisher={Springer Nature},
  doi={10.1038/s41593-026-02202-6},
  url={https://doi.org/10.1038/s41593-026-02202-6}
}

License

This project is licensed for non-commercial academic research use only. Commercial use is not permitted without a separate license from Mass General Brigham.

For commercial licensing inquiries, please contact the Mass General Brigham Office of Technology Development. See LICENSE for details.