  Compared to natural images, medical images usually show stronger visual
patterns and therefore this adds flexibility and elasticity to resource-limited
clinical applications by injecting proper priors into neural networks. In this
paper, we propose spatially covariant pixel-aligned classifier (SCP) to improve
the computational efficiency and meantime maintain or increase accuracy for
lesion segmentation. SCP relaxes the spatial invariance constraint imposed by
convolutional operations and optimizes an underlying implicit function that
maps image coordinates to network weights, the parameters of which are obtained
along with the backbone network training and later used for generating network
weights to capture spatially covariant contextual information. We demonstrate
the effectiveness and efficiency of the proposed SCP using two lesion
segmentation tasks from different imaging modalities: white matter
hyperintensity segmentation in magnetic resonance imaging and liver tumor
segmentation in contrast-enhanced abdominal computerized tomography. The
network using SCP has achieved 23.8%, 64.9% and 74.7% reduction in GPU memory
usage, FLOPs, and network size with similar or better accuracy for lesion
segmentation.
