Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016

Abstract

The tool presence detection challenge at M2CAI 2016 consists of identifying the presence/absence of seven surgical tools in the images of cholecystectomy videos. Here, we propose to use deep architectures that are based on our previous work where we presented several architectures to perform multiple recognition tasks on laparoscopic videos. In this technical report, we present the tool presence detection results using two architectures: (1) a single-task architecture designed to perform solely the tool presence detection task and (2) a multi-task architecture designed to perform jointly phase recognition and tool presence detection. The results show that the multi-task network only slightly improves the tool presence detection results. In constrast, a significant improvement is obtained when there are more data available to train the networks. This significant improvement can be regarded as a call for action for other institutions to start working toward publishing more datasets into the community, so that better models could be generated to perform the task.

The dataset is available at http://camma.u-strasbg.fr/m2cai2016/ . arXiv admin note: text overlap with arXiv:1610.08844

References in corpus (1)

• Caffe: Convolutional Architecture for Fast Feature Embedding

Cited by in corpus (6)

• Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition
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• LapTool-Net: A Contextual Detector of Surgical Tools in Laparoscopic Videos Based on Recurrent Convolutional Neural Networks
• Workflow Augmentation of Video Data for Event Recognition with Time-Sensitive Neural Networks
• A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis
• Real-time Surgical Tools Recognition in Total Knee Arthroplasty Using Deep Neural Networks