Image Complexity Guided Network Compression for Biomedical Image Segmentation

TL;DR

This paper introduces an image complexity-guided method for efficiently compressing biomedical image segmentation networks, enabling quick estimation of optimal compressed models without extensive training.

Contribution

It proposes a novel framework that maps dataset complexity to accuracy degradation, allowing rapid prediction of compressed network performance for resource-constrained scenarios.

Findings

Retains up to 95% of original segmentation accuracy

Achieves approximately 32x reduction in trainable weights

Effective across multiple datasets and CNN architectures

Abstract

Compression is a standard procedure for making convolutional neural networks (CNNs) adhere to some specific computing resource constraints. However, searching for a compressed architecture typically involves a series of time-consuming training/validation experiments to determine a good compromise between network size and performance accuracy. To address this, we propose an image complexity-guided network compression technique for biomedical image segmentation. Given any resource constraints, our framework utilizes data complexity and network architecture to quickly estimate a compressed model which does not require network training. Specifically, we map the dataset complexity to the target network accuracy degradation caused by compression. Such mapping enables us to predict the final accuracy for different network sizes, based on the computed dataset complexity. Thus, one may choose a solution that meets both the network size and segmentation accuracy requirements. Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network. We conduct experiments using 5 datasets, employing 3 commonly-used CNN architectures for biomedical image segmentation as representative networks. Our proposed framework is shown to be effective for generating compressed segmentation networks, retaining up to $\approx 95\%$ of the full-sized network segmentation accuracy, and at the same time, utilizing $\approx 32x$ fewer network trainable weights (average reduction) of the full-sized networks.