$\mathsf{CSMAE~}$:~Cataract Surgical Masked Autoencoder (MAE) based Pre-training
Journal:
arXiv
Published Date:
Feb 12, 2025
Abstract
Automated analysis of surgical videos is crucial for improving surgical
training, workflow optimization, and postoperative assessment. We introduce a
CSMAE, Masked Autoencoder (MAE)-based pretraining approach, specifically
developed for Cataract Surgery video analysis, where instead of randomly
selecting tokens for masking, they are selected based on the spatiotemporal
importance of the token. We created a large dataset of cataract surgery videos
to improve the model's learning efficiency and expand its robustness in
low-data regimes. Our pre-trained model can be easily adapted for specific
downstream tasks via fine-tuning, serving as a robust backbone for further
analysis. Through rigorous testing on a downstream step-recognition task on two
Cataract Surgery video datasets, D99 and Cataract-101, our approach surpasses
current state-of-the-art self-supervised pretraining and adapter-based transfer
learning methods by a significant margin. This advancement not only
demonstrates the potential of our MAE-based pretraining in the field of
surgical video analysis but also sets a new benchmark for future research.
