  Surgical tool detection is essential for analyzing and evaluating minimally
invasive surgery videos. Current approaches are mostly based on supervised
methods that require large, fully instance-level labels (i.e., bounding boxes).
However, large image datasets with instance-level labels are often limited
because of the burden of annotation. Thus, surgical tool detection is important
when providing image-level labels instead of instance-level labels since
image-level annotations are considerably more time-efficient than
instance-level annotations. In this work, we propose to strike a balance
between the extremely costly annotation burden and detection performance. We
further propose a co-occurrence loss, which considers a characteristic that
some tool pairs often co-occur together in an image to leverage image-level
labels. Encapsulating the knowledge of co-occurrence using the co-occurrence
loss helps to overcome the difficulty in classification that originates from
the fact that some tools have similar shapes and textures. Extensive
experiments conducted on the Endovis2018 dataset in various data settings show
the effectiveness of our method.
