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

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