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Convolutional Neural Networks (CNNs) are the predominant model used for avariety of medical image analysis tasks. At inference time, these models arecomputationally intensive, especially with volumetric data. In principle, it ispossible to trade accuracy for computational efficiency by manipulating therescaling factor in the downsample and upsample layers of CNN architectures.However, properly exploring the accuracy-efficiency trade-off is prohibitivelyexpensive with existing models. To address this, we introduce Scale-SpaceHyperNetworks (SSHN), a method that learns a spectrum of CNNs with varyinginternal rescaling factors. A single SSHN characterizes an entire Paretoaccuracy-efficiency curve of models that match, and occasionally surpass, theoutcomes of training many separate networks with fixed rescaling factors. Wedemonstrate the proposed approach in several medical image analysisapplications, comparing SSHN against strategies with both fixed and dynamicrescaling factors. We find that SSHN consistently provides a betteraccuracy-efficiency trade-off at a fraction of the training cost. Trained SSHNsenable the user to quickly choose a rescaling factor that appropriatelybalances accuracy and computational efficiency for their particular needs atinference.

Scale-Space Hypernetworks for Efficient Biomedical Imaging