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MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation

Source code for "MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation", MIDL 2023, https://openreview.net/forum?id=jt-ochRhqG.

Usage

Pretrain your Image Encoder model locally using the dummy dataset jointly with Bio+Clinical BERT Text Encoder on your generated text annotations in data/dummy_dataframes/gpt_augs.txt. Modify config_dataset/make_dataset_setting_file.py to make a dataset file containing the file_path_to_image, text annotations pairs.

python main_train.py --exams D15 D30 M3 M12 --architecture RN50 --context_length 77 --pretrained_biobert 1 --pretrained_dir RN50.pt --img_size 96 144 192 --batch_size 22 --eval_every 1 --learning_rate 1e-4 --num_epochs 200 --warmup_epochs 40 --freeze_nlp first11 --use_amp 1 --num_workers 2 --gradient_accumulation_steps 1 --description dummy_MEDIMP --wandb_id dummy_test

Pretrain your Image Encoder model sending a slurm job. Edit the file to modify the slurm parameters and/or the main_train.py arguments.

python slurm_train_features.py

Dummy dataset

As the dataset for this work is not publicly available, I built a dummy mri dataset path tree similar to our dataset so that the code can be ran on it, when argument dummy=True in get_patient_seq_paths function.

├── data
│   ├── dummy_dataframes
│   │   ├── df_clinicobiological_data.csv
│   │   ├── gtp_augs.txt
│   ├── dummy_mri_dataset (contains patients)
│   │   ├── dummy_mri.nii.gz
│   │   ├── 001-0001-A-A (contains exams)
│   │   │   ├── D15 (contains MRI sequences)
│   │   │   │   ├── 1_WATER_AX_LAVA-Flex_ss_IV
│   │   │   │   ├── 2_WATER_AX_LAVA-Flex_ART
│   │   │   │   ├── 3_WATER_AX_LAVA-Flex_tub
│   │   │   ├── D30
│   │   │   ├── M3
│   │   │   ├── M12
│   │   ├── 001-0002-B-B
│   │   ├── ...
└── ...

Requirements

See conda_environment.yml file or replicate the conda env:

conda env create -n ENVNAME --file conda_environment.yml

References

@misc{milecki2023medimp,
      title={MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation}, 
      author={Leo Milecki and Vicky Kalogeiton and Sylvain Bodard and Dany Anglicheau and Jean-Michel Correas and Marc-Olivier Timsit and Maria Vakalopoulou},
      year={2023},
      eprint={2303.12445},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}