Dr. Ehsan Azimi Profile

Dr. Ehsan Azimi

at The Univ. of Arizona

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Proceedings Article | 7 June 2024 Presentation + Paper

Exploring action recognition in endoscopy video datasets

Yuchen Tian, Sidike Paheding, Ehsan Azimi, Eung-Joo Lee

Proceedings Volume 13034, 130340D (2024) https://doi.org/10.1117/12.3014345

KEYWORDS: Video, Action recognition, Data modeling, Endoscopy, Education and training, Light sources and illumination, Equipment, Performance modeling, Neural networks, Machine learning

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Surgical image and video applications using endoscopic datasets have been actively investigated to develop advanced surgical assistant systems. These applications are particularly crucial for understanding surgical scenes during procedures. Specifically, segmentation techniques allow for identifying anatomical structures and surgical instruments, while quality control methods refine surgical techniques, and action recognition aids in discerning surgical steps. A significant improvement in performance across different downstream tasks has been achieved due to the advancements in deep neural networks and the expansive training dataset available. However, the exploration of surgical action recognition remains limited. Existing methods face challenges in real-world settings, mainly due to the lack of adaptability in a dynamic imaging environment. In this study, we present a framework for surgical action recognition in endoscopic datasets by leveraging video-masked autoencoders (VideoMAE), which has shown promise in video dataset analysis with minimal datasets. Additionally, we incorporate a temporal data augmentation technique to represent diverse imaging conditions and resolve the issue of using single-source data with low quality. For our experiments, we utilize VideoMAE v2 pre-trained on Unlabeled Hybrid datasets and fine-tune the model on the CholecT45 dataset for validation. Our proposed method shows the effectiveness of using the VideoMAE structure with focal loss, particularly for action recognition tasks in surgical scenarios.

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