  Convolutional neural networks (CNNs) for biomedical image analysis are often
of very large size, resulting in high memory requirement and high latency of
operations. Searching for an acceptable compressed representation of the base
CNN for a specific imaging application typically involves a series of
time-consuming training/validation experiments to achieve a good compromise
between network size and accuracy. To address this challenge, we propose
CC-Net, a new image complexity-guided CNN compression scheme for biomedical
image segmentation. Given a CNN model, CC-Net predicts the final accuracy of
networks of different sizes based on the average image complexity computed from
the training data. It then selects a multiplicative factor for producing a
desired network with acceptable network accuracy and size. Experiments show
that CC-Net is effective for generating compressed segmentation networks,
retaining up to 95% of the base network segmentation accuracy and utilizing
only 0.1% of trainable parameters of the full-sized networks in the best case.
