Neural Network Pruning for Biomedical Image Segmentation

Instead of manual segmentation, Segmentation using Artificial Neural Networks is very useful in biomedical image analysis. However, deploying artificial neural networks requires large memory footprint and computational costs. In this work, we propose a pruning approach to alleviate these requirements for the U-Net, which is the most popular segmentation neural network for biomedical image. Our approach handles upsampling layers and skip connections, which are essential components in U-Net architecture. We show that our approach achieves 2x speedup, more than 7x size reduction with less than 2% loss in average intersection-over-union (IOU) on PhC-U373 and DIC-HeLa biomedical data set.