  Our work proposes neural network design choices that set the state-of-the-art
on a challenging public benchmark on cataract surgery, CaDIS. Our methodology
achieves strong performance across three semantic segmentation tasks with
increasingly granular surgical tool class sets by effectively handling class
imbalance, an inherent challenge in any surgical video. We consider and
evaluate two conceptually simple data oversampling methods as well as different
loss functions. We show significant performance gains across network
architectures and tasks especially on the rarest tool classes, thereby
presenting an approach for achieving high performance when imbalanced granular
datasets are considered. Our code and trained models are available at
https://github.com/RViMLab/MICCAI2021_Cataract_semantic_segmentation and
qualitative results on unseen surgical video can be found at
https://youtu.be/twVIPUj1WZM.
