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

CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation