  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.
