  Accurate and efficient lumbar spine disease identification is crucial for
clinical diagnosis. However, existing deep learning models with millions of
parameters 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 a
multi-scale context-guided network with coarse-to-fine localization and
classification, named CCF-Net, for lumbar spine disease identification.
Specifically, in learning, we divide the localization objective into two
parallel tasks, coarse and fine, which are more straightforward and effectively
reduce the number of parameters and computational cost. The experimental
results show that the coarse-to-fine design presents the potential to achieve
high performance with fewer parameters and data requirements. Moreover, the
multi-scale context-guided module can significantly improve the performance by
6.45% and 5.51% with ResNet18 and ResNet50, respectively. Our code is available
at https://github.com/czifan/CCFNet.pytorch.
