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Accurate and efficient lumbar spine disease identification is crucial forclinical diagnosis. However, existing deep learning models with millions ofparameters often fail to learn with only hundreds or dozens of medical images.These models also ignore the contextual relationship between adjacent objects,such as between vertebras and intervertebral discs. This work introduces amulti-scale context-guided network with coarse-to-fine localization andclassification, named CCF-Net, for lumbar spine disease identification.Specifically, in learning, we divide the localization objective into twoparallel tasks, coarse and fine, which are more straightforward and effectivelyreduce the number of parameters and computational cost. The experimentalresults show that the coarse-to-fine design presents the potential to achievehigh performance with fewer parameters and data requirements. Moreover, themulti-scale context-guided module can significantly improve the performance by6.45% and 5.51% with ResNet18 and ResNet50, respectively. Our code is availableat https://github.com/czifan/CCFNet.pytorch.

Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification