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Master thesis and AACL-IJCNLP Codebase

Installation

To run, first install the requirements for python3.8 in the requirements.txt.

Download MIMIC-CXR first (and Chexpert, covidx, and rsna for external validation).

Data preprocessing

Run preprocess_chexpert.ipynb, preprocess_covidx.ipynb, preprocess_rsna.ipynb. Adjust the base path at the start to your download location. The path for MIMIC-CXR also needs to be adapted in dataset_utils.pyin line 21 .

The eda.ipynb script can be used for some data exploration.

Training

The contrastive_learning.ipynb notebook takes care of the contrastive learning. The sl.py fine-tunes the whole network. The scripts lin_probe_sklearn.pyand lin_probe_multi.py train linear probes on top of the CLIP features.

Evaluation

If you have trained all the models from the paper, the eval_cl.ipynb and eval_sl.ipynb notebooks will create all the plots and figures from the paper (except the text2image results).