  Spinal degeneration plagues many elders, office workers, and even the younger
generations. Effective pharmic or surgical interventions can help relieve
degenerative spine conditions. However, the traditional diagnosis procedure is
often too laborious. Clinical experts need to detect discs and vertebrae from
spinal magnetic resonance imaging (MRI) or computed tomography (CT) images as a
preliminary step to perform pathological diagnosis or preoperative evaluation.
Machine learning systems have been developed to aid this procedure generally
following a two-stage methodology: first perform anatomical localization, then
pathological classification. Towards more efficient and accurate diagnosis, we
propose a one-stage detection framework termed SpineOne to simultaneously
localize and classify degenerative discs and vertebrae from MRI slices.
SpineOne is built upon the following three key techniques: 1) a new design of
the keypoint heatmap to facilitate simultaneous keypoint localization and
classification; 2) the use of attention modules to better differentiate the
representations between discs and vertebrae; and 3) a novel gradient-guided
objective association mechanism to associate multiple learning objectives at
the later training stage. Empirical results on the Spinal Disease Intelligent
Diagnosis Tianchi Competition (SDID-TC) dataset of 550 exams demonstrate that
our approach surpasses existing methods by a large margin.
