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Revisiting CLIP: Efficient Alignment of 3D MRI and Tabular Data using Domain-Specific Foundation Models

Official Pytorch implementation from the paper

Revisiting CLIP: Efficient Alignment of 3D MRI and Tabular Data using Domain-Specific Foundation Models
ISBI 2025
Jakob Krogh Petersen*, Valdemar Licht*, Mads Nielsen, Asbjørn Munk

Pioneer Centre for AI & University of Copenhagen

* Equal Contribution

Paper link: ArXiv.

tsne_final

Getting Started

  1. Install Poetry.
  2. Create environment by calling poetry install.
  3. Setup environment variables. Run touch .env. Then add the following to the new .env file:
export YUCCA_SOURCE=link/to/datasets
export YUCCA_RAW_DATA=link/to/raw_data
export YUCCA_PREPROCESSED_DATA=link/to/preprocessed_data
export YUCCA_MODELS=link/to/models
export YUCCA_RESULTS=link/to/results
export STATES=src/models/states

  1. Ensure the states folder exists by running mkdir -p src/models/states. This folder contains weights and vocabularies for required models.

  2. Create a 'GammaKnife' folder at the chosen YUCCA_SOURCE path. Add the 'Brain-TR-GammaKnife-processed' folder that you download from here to the 'GammaKnife' folder.

  3. Run Task Conversion. Given the setup as above one can run the task conversion task with the corresponding script bash run_task_conversion.sh

  4. Run Preprocessing. Run the preprocessing step using the corresponding script bash run_preprocess.sh

  5. Run Training. Run training with the script bash run_train.sh. Use arguments -e and -c to add experiment settings and configuration settings. Use -f to train from scratch. For example, to train from scratch with an experiment locally use:

bash run_train.bash -e 16x_swinT_k0 -c local -f

Model checkpoints

We release checkpoints for the best-performing CLIP-trained models for each of the studied vision architectures, as well as the pre-trained models, that we perform the CLIP training.

CLIP-trained checkpoints

Vision Model Parameters (M) Checkpoint
Swin-T 8 Download
MedNeXt 4 Download
ResNet 57 Download

Pre-trained models

Model Parameters (M) Checkpoint
Bert 110 Download*
Swin-T 8 Download
MedNeXt 4 Download**
ResNet 57 Download**

*The official model weights can be extracted from HuggingFace. See here for the vocabulary.

**For the MedNeXt and ResNet models, we refer to AMAES.

Citation

Please use

@article{krogh2025clip3d,
  title={Efficient Alignment of 3D MRI and Tabular Data using Domain-Specific Foundation Models},
  author={Petersen, Jakob Krogh and Licht, Johan Valdemar and Nielsen, Mads and Munk, Asbjørn},
  journal={arXiv preprint arXiv:2501.14051},
  year={2025}
}