Pre-training on High Definition X-ray Images: An Experimental Study
<|reference_start|>Pre-training on High Definition X-ray Images: An Experimental Study: Existing X-ray based pre-trained vision models are usually conducted on a relatively small-scale dataset (less than 500k samples) with limited resolution (e.g., 224 $\times$ 224). However, the key to the success of self-supervised pre-training large models lies in massive training data, and maintaining high resolution in the field of X-ray images is the guarantee of effective solutions to difficult miscellaneous diseases. In this paper, we address these issues by proposing the first high-definition (1280 $\times$ 1280) X-ray based pre-trained foundation vision model on our newly collected large-scale dataset which contains more than 1 million X-ray images. Our model follows the masked auto-encoder framework which takes the tokens after mask processing (with a high rate) is used as input, and the masked image patches are reconstructed by the Transformer encoder-decoder network. More importantly, we introduce a novel context-aware masking strategy that utilizes the chest contour as a boundary for adaptive masking operations. We validate the effectiveness of our model on two downstream tasks, including X-ray report generation and disease recognition. Extensive experiments demonstrate that our pre-trained medical foundation vision model achieves comparable or even new state-of-the-art performance on downstream benchmark datasets. The source code and pre-trained models of this paper will be released on https://github.com/Event-AHU/Medical_Image_Analysis.<|reference_end|>
@article{wang2024pre-training,
    title={Pre-training on High Definition X-ray Images: An Experimental Study},
    author={Xiao Wang, Yuehang Li, Wentao Wu, Jiandong Jin, Yao Rong, Bo Jiang,
  Chuanfu Li, Jin Tang},
    journal={arXiv preprint arXiv:2404.17926},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17926},
    primaryClass={eess.IV cs.AI cs.CV cs.LG}
}