  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.
