Self-supervised and supervised pretraining for chest radiograph AI

Papers in this repository

1. Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images
   European Radiology Experimental, 2024.
   DOI: https://doi.org/10.1186/s41747-023-00411-3

2. High-resolution self-supervised learning with DINOv3 advances chest radiograph analysis

Prerequisites

The software is developed in Python 3.9. For deep learning, the PyTorch 2.8 framework is used.

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

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

Note: This might take a few minutes.

---

Model initializations used

ImageNet (supervised):

• ViT-B/16 (via timm): vit_base_patch16_224_in21k
  https://github.com/huggingface/pytorch-image-models

DINOv2 (self-supervised):

• ViT-B/16: https://huggingface.co/facebook/dinov2-base

DINOv3 (self-supervised):

• ViT-B/16: https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m
• ConvNeXt-B: https://huggingface.co/facebook/dinov3-convnext-base-pretrain-lvd1689m
• ViT-7B/16 (frozen features): https://huggingface.co/facebook/dinov3-vit7b16-pretrain-lvd1689m
  (ConvNeXt DINOv3 weights were loaded from SafeTensors.)

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Code structure

• main_vitmed.py — single entry point for training/evaluation.
• configs/config.yaml — edit data paths, preprocessing, model/backbone, initialization (ImageNet / DINOv2 / DINOv3), resolution (224 / 512), optimizer and schedule.
• data/ — dataset I/O, preprocessing, augmentation.
• Train_Valid_vitmed.py — training / validation loops.
• Prediction_vitmed.py — inference & metrics.

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Quickstart

1. Prepare datasets following the paths and splits in configs/config.yaml.
2. Choose an experiment name; the script will create a folder with checkpoints, metrics, TensorBoard logs, and a copy of the effective config.
3. Launch training/evaluation from the project root, e.g.

python main_vitmed.py --config ./configs/config.yaml --experiment dinov3_convnext_512

In case you use this repository, please cite the original paper:

If you use this code, please cite both papers:

Paper 1

S. Tayebi Arasteh, L. Misera, J.N. Kather, D. Truhn, S. Nebelung. Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images. European Radiology Experimental 8, 10 (2024). https://doi.org/10.1186/s41747-023-00411-3

BibTex

@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}
}

Paper 2

S. Tayebi Arasteh, et al. High-resolution self-supervised learning with DINOv3 advances chest radiograph analysis. 2025.

@article{dinov3_cxr_2025,
  author  = {Tayebi Arasteh, Soroosh and others},
  title   = {High-resolution self-supervised learning with DINOv3 advances chest radiograph analysis},
  year    = {2025},
  doi     = {},
  url     = {}
}