EndoViT: pretraining vision transformers on a large collection of endoscopic images.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: Automated endoscopy video analysis is essential for assisting surgeons during medical procedures, but it faces challenges due to complex surgical scenes and limited annotated data. Large-scale pretraining has shown great success in natural language processing and computer vision communities in recent years. These approaches reduce the need for annotated data, which is of great interest in the medical domain. In this work, we investigate endoscopy domain-specific self-supervised pretraining on large collections of data.

Authors

  • Dominik Batić
    Chair for Computer Aided Medical Procedures, Technical University Munich, Munich, Germany.
  • Felix Holm
    Technical University Munich, Germany.
  • Ege Özsoy
    Technical University Munich, Germany.
  • Tobias Czempiel
    Technical University Munich, Germany.
  • Nassir Navab
    Chair for Computer Aided Medical Procedures & Augmented Reality, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.