  Computer-assisted treatment has emerged as a viable application of medical
imaging, owing to the efficacy of deep learning models. Real-time inference
speed remains a key requirement for such applications to help medical
personnel. Even though there generally exists a trade-off between performance
and model size, impressive efforts have been made to retain near-original
performance by compromising model size. Neural network pruning has emerged as
an exciting area that aims to eliminate redundant parameters to make the
inference faster. In this study, we show an application of neural network
pruning in polyp segmentation. We compute the importance score of convolutional
filters and remove the filters having the least scores, which to some value of
pruning does not degrade the performance. For computing the importance score,
we use the Taylor First Order (TaylorFO) approximation of the change in network
output for the removal of certain filters. Specifically, we employ a
gradient-normalized backpropagation for the computation of the importance
score. Through experiments in the polyp datasets, we validate that our approach
can significantly reduce the parameter count and FLOPs retaining similar
performance.
