  Differentiating Vertebral Compression Fractures (VCFs) associated with trauma
and osteoporosis (benign VCFs) or those caused by metastatic cancer (malignant
VCFs) are critically important for treatment decisions. So far, automatic VCFs
diagnosis is solved in a two-step manner, i.e. first identify VCFs and then
classify it into benign or malignant. In this paper, we explore to model VCFs
diagnosis as a three-class classification problem, i.e. normal vertebrae,
benign VCFs, and malignant VCFs. However, VCFs recognition and classification
require very different features, and both tasks are characterized by high
intra-class variation and high inter-class similarity. Moreover, the dataset is
extremely class-imbalanced. To address the above challenges, we propose a novel
Two-Stream Compare and Contrast Network (TSCCN) for VCFs diagnosis. This
network consists of two streams, a recognition stream which learns to identify
VCFs through comparing and contrasting between adjacent vertebra, and a
classification stream which compares and contrasts between intra-class and
inter-class to learn features for fine-grained classification. The two streams
are integrated via a learnable weight control module which adaptively sets
their contribution. The TSCCN is evaluated on a dataset consisting of 239 VCFs
patients and achieves the average sensitivity and specificity of 92.56\% and
96.29\%, respectively.
