  Automatic tool detection from surgical imagery has a multitude of useful
applications, such as real-time computer assistance for the surgeon. Using the
successful residual network architecture, a system that can distinguish 21
different tools in cataract surgery videos is created. The videos are provided
as part of the 2017 CATARACTS challenge and pose difficulties found in many
real-world datasets, for example a strong class imbalance. The construction of
the detection system is guided by a wide array of experiments that explore
different design decisions.
