Deep learning based multi-label classification for surgical tool presence detection in laparoscopic videos

Automatic recognition of surgical workflow is an unresolved problem among the community of computer-assisted interventions. Among all the features used for surgical workflow recognition, one important feature is the presence of the surgical tools. Extracting this feature leads to the surgical tool presence detection problem to detect what tools are used at each time in surgery. This paper proposes a deep learning based multi-label classification method for surgical tool presence detection in laparoscopic videos. The proposed method combines two state-of-the-art deep neural networks and uses ensemble learning to solve the tool presence detection problem as a multi-label classification problem. The performance of the proposed method has been evaluated in the surgical tool presence detection challenge held by Modeling and Monitoring of Computer Assisted Interventions workshop. The proposed method shows superior performance compared to other methods and has won the first place of the challenge.