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Simmonstt/
BrainAnytime

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BrainAnytime Checkpoints

Finetuned checkpoints for BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability.

Paper

Title: BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability
arXiv: 2605.13059
PDF: https://arxiv.org/pdf/2605.13059

Model Sources

Repository: https://github.com/guangqianyang/BrainAnytime
Paper: https://arxiv.org/abs/2605.13059
Demo: https://huggingface.co/spaces/Simmonstt/BrainAnytime-Demo

Checkpoints

File	Task	Type
`CN_vs_AD_seed_0_best.pth`	CN vs AD	Classification
`CN_vs_MCI_seed_0_best.pth`	CN vs MCI	Classification
`MMSE_seed_0_best.pth`	MMSE	Regression
`AGE_seed_0_best.pth`	AGE	Regression

Usage

python

from huggingface_hub import hf_hub_download

ckpt = hf_hub_download(
    repo_id="Simmonstt/BrainAnytime",
    filename="CN_vs_AD_seed_0_best.pth",
)

Citation

If you use these checkpoints, please cite:

bibtex

@misc{yang2026brainanytimeanatomyawarecrossmodalpretraining,
      title={BrainAnytime: Anatomy-Aware Cross-Modal Pretraining for Brain Image Analysis with Arbitrary Modality Availability},
      author={Guangqian Yang and Tong Ding and Wenlong Hou and Yue Xun and Ye Du and Qian Niu and Shujun Wang},
      year={2026},
      eprint={2605.13059},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.13059},
}

Paper page: https://arxiv.org/abs/2605.13059