  Automated surgical workflow analysis and understanding can assist surgeons to
standardize procedures and enhance post-surgical assessment and indexing, as
well as, interventional monitoring. Computer-assisted interventional (CAI)
systems based on video can perform workflow estimation through surgical
instruments' recognition while linking them to an ontology of procedural
phases. In this work, we adopt a deep learning paradigm to detect surgical
instruments in cataract surgery videos which in turn feed a surgical phase
inference recurrent network that encodes temporal aspects of phase steps within
the phase classification. Our models present comparable to state-of-the-art
results for surgical tool detection and phase recognition with accuracies of 99
and 78% respectively.
