  The surgical workflow challenge at M2CAI 2016 consists of identifying 8
surgical phases in cholecystectomy procedures. 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 phase recognition results using two
architectures: (1) a single-task architecture designed to perform solely the
surgical phase recognition task and (2) a multi-task architecture designed to
perform jointly phase recognition and tool presence detection. On top of these
architectures we propose to use two different approaches to enforce the
temporal constraints of the surgical workflow: (1) HMM-based and (2) LSTM-based
pipelines. The results show that the LSTM-based approach is able to outperform
the HMM-based approach and also to properly enforce the temporal constraints
into the recognition process.
