Anatomical Foundation Models for Brain MRIs

Installing

pip3 install git+https://github.com/carloalbertobarbano/AnatCL.git

Pretrained Models

import torch
from torchvision import transforms
from anatcl import AnatCL

model = AnatCL(descriptor="global", fold=0, pretrained=True)
model = model.to("cuda")

transform = transforms.Compose([
        transforms.Lambda(lambda x: torch.from_numpy(x.copy()).float()),
        transforms.Normalize(mean=0.0, std=1.0)
])

# Volumes should be 121x128x121 preprocessed with cat12 toolbox (vbm)
dataset = Dataset(transform=transform, ...)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False,
                                         num_workers=8, persistent_workers=True)

model.eval()
for (image, label) in dataloader:
    image = image.to("cuda")
    output = model(image)
    
    # Do something with the output

Training Code

Coming soon

Testing code

Coming soon

Citing

If you find our models useful please do not forget to cite this work as

@article{barbano2026anatomical,
  title = {Anatomical foundation models for brain MRIs},
  journal = {Pattern Recognition Letters},
  volume = {199},
  pages = {178-184},
  year = {2026},
  issn = {0167-8655},
  doi = {https://doi.org/10.1016/j.patrec.2025.11.028},
  url = {https://www.sciencedirect.com/science/article/pii/S0167865525003848},
  author = {Carlo Alberto Barbano and Matteo Brunello and Benoit Dufumier and Marco Grangetto},
}