General surgery vision transformer: A video pre-trained foundation model for general surgery
<|reference_start|>General surgery vision transformer: A video pre-trained foundation model for general surgery: The absence of openly accessible data and specialized foundation models is a major barrier for computational research in surgery. Toward this, (i) we open-source the largest dataset of general surgery videos to-date, consisting of 680 hours of surgical videos, including data from robotic and laparoscopic techniques across 28 procedures; (ii) we propose a technique for video pre-training a general surgery vision transformer (GSViT) on surgical videos based on forward video prediction that can run in real-time for surgical applications, toward which we open-source the code and weights of GSViT; (iii) we also release code and weights for procedure-specific fine-tuned versions of GSViT across 10 procedures; (iv) we demonstrate the performance of GSViT on the Cholec80 phase annotation task, displaying improved performance over state-of-the-art single frame predictors.<|reference_end|>
@article{schmidgall2024general,
    title={General surgery vision transformer: A video pre-trained foundation model
  for general surgery},
    author={Samuel Schmidgall, Ji Woong Kim, Jeffrey Jopling, Axel Krieger},
    journal={arXiv preprint arXiv:2403.05949},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05949},
    primaryClass={cs.CV cs.LG q-bio.TO}
}