Presence Detection of Surgical Tool Via Densely Connected Convolutional Networks

Abstract

Surgical tool detection is important to surgical workflow recognition. It is considered as an essential task in surgical phase recognition. Recently, Densely Connected Convolutional Networks have gained a huge success in computer vision applications, especially in object detection and image Classification. In this paper, we proposed a method to solve the surgical tool presence detection problem as a multi-label classification problem based on Densely Connected Convolutional Networks. The performance of the proposed method has been evaluated in the surgical tool presence detection challenge dataset held by Modeling and Monitoring of Computer Assisted Interventions workshop. The result shows that our proposed model has achieved significant success in detecting surgical tool and got a mean average precision of 62.9% on the testing data. The technology studied in this paper has broad application prospects in computer-aided surgical systems and is a core component of the artificial intelligence medical operating room in the future.