  Minimally invasive surgeries and related applications demand surgical tool
classification and segmentation at the instance level. Surgical tools are
similar in appearance and are long, thin, and handled at an angle. The
fine-tuning of state-of-the-art (SOTA) instance segmentation models trained on
natural images for instrument segmentation has difficulty discriminating
instrument classes. Our research demonstrates that while the bounding box and
segmentation mask are often accurate, the classification head mis-classifies
the class label of the surgical instrument. We present a new neural network
framework that adds a classification module as a new stage to existing instance
segmentation models. This module specializes in improving the classification of
instrument masks generated by the existing model. The module comprises
multi-scale mask attention, which attends to the instrument region and masks
the distracting background features. We propose training our classifier module
using metric learning with arc loss to handle low inter-class variance of
surgical instruments. We conduct exhaustive experiments on the benchmark
datasets EndoVis2017 and EndoVis2018. We demonstrate that our method
outperforms all (more than 18) SOTA methods compared with, and improves the
SOTA performance by at least 12 points (20%) on the EndoVis2017 benchmark
challenge and generalizes effectively across the datasets.
