• This is the official repository of the paper Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images.
• Update: We have released our fine-tuned network weights for research purposes. Check this link! (Size: 1.6 GB)

Pre-training datasets, like ImageNet, have become the gold standard in medical image analysis. However, the emergence of self-supervised learning (SSL), which leverages unlabeled data to learn robust features, presents an opportunity to bypass the intensive labeling process. In this study, we explored if SSL for pre-training on non-medical images can be applied to chest radiographs and how it compares to supervised pre-training on non-medical images and on medical images. We utilized a vision transformer and initialized its weights based on (i) SSL pre-training on natural images (DINOv2), (ii) SL pre-training on natural images (ImageNet dataset), and (iii) SL pre-training on chest radiographs from the MIMIC-CXR database. We tested our approach on over 800,000 chest radiographs from six large global datasets, diagnosing more than 20 different imaging findings. Our SSL pre-training on curated images not only outperformed ImageNet-based pre-training (P<0.001 for all datasets) but, in certain cases, also exceeded SL on the MIMIC-CXR dataset. Our findings suggest that selecting the right pre-training strategy, especially with SSL, can be pivotal for improving artificial intelligence (AI)'s diagnostic accuracy in medical imaging. By demonstrating the promise of SSL in chest radiograph analysis, we underline a transformative shift towards more efficient and accurate AI models in medical imaging.

The software is developed in Python 3.9. For the deep learning, the PyTorch 2.0 framework is used.

Main Python modules required for the software can be installed from ./requirements:

$ conda env create -f requirements.yaml
$ conda activate enhancingpaper

Note: This might take a few minutes.

Our source code for training and evaluation of the deep neural networks, image analysis and preprocessing, and data augmentation are available here.

1. Everything can be run from ./main_vitmed.py.

• The data preprocessing parameters, directories, hyper-parameters, and model parameters can be modified from ./configs/config.yaml.
• Also, you should first choose an experiment name (if you are starting a new experiment) for training, in which all the evaluation and loss value statistics, tensorboard events, and model & checkpoints will be stored. Furthermore, a config.yaml file will be created for each experiment storing all the information needed.
• For testing, just load the experiment which its model you need.

2. The rest of the files:

• ./data/ directory contains all the data preprocessing, augmentation, and loading files.
• ./Train_Valid_vitmed.py contains the training and validation processes.
• ./Prediction_vitmed.py all the prediction and testing processes.

S. Tayebi Arasteh, L. Misera, J.N. Kather et al. Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images. Eur Radiol Exp 8, 10 (2024). https://doi.org/10.1186/s41747-023-00411-3.

@article {enhancingarasteh,
  author = {Tayebi Arasteh, Soroosh and Misera, Leo and Kather, Jakob Nikolas and Truhn, Daniel and Nebelung, Sven},
  title = {Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images},
  year = {2024},
  volume = {8},
  number = {10},
  doi = {10.1186/s41747-023-00411-3},
  publisher = {Springer},
  URL = {https://doi.org/10.1186/s41747-023-00411-3},
  journal = {European Radiology Experimental}
}