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.

翻译：计算机辅助诊疗凭借深度学习模型的有效性，已成为医学成像领域的一项可行应用。实时推理速度依然是此类辅助医务人员应用的关键需求。尽管性能与模型规模之间通常存在权衡关系，但研究者已通过牺牲模型规模的努力，成功保留了近乎原始的性能。神经网络剪枝作为新兴研究方向，旨在消除冗余参数以提升推理速度。本研究展示了神经网络剪枝在息肉分割中的应用：我们计算卷积滤波器的贡献度得分，并移除得分最低的滤波器——在特定剪枝阈值内，该方法不会导致性能下降。为计算贡献度得分，我们采用泰勒一阶近似（Taylor First Order, TaylorFO）估计移除特定滤波器时网络输出的变化量，具体通过梯度归一化反向传播实现贡献度得分的计算。基于息肉数据集的实验验证表明，该方法能在保持相似性能的前提下显著减少参数量和浮点运算次数（FLOPs）。