Two-Stream Compare and Contrast Network for Vertebral Compression Fracture Diagnosis
This work addresses a critical medical imaging problem for clinicians by improving automated diagnosis of VCFs, though it is incremental as it builds on prior two-step methods.
The paper tackles the problem of automatically diagnosing Vertebral Compression Fractures (VCFs) by modeling it as a three-class classification task (normal, benign, malignant) and proposes a Two-Stream Compare and Contrast Network (TSCCN) to address challenges like high intra-class variation and class imbalance, achieving an average sensitivity of 92.56% and specificity of 96.29% on a dataset of 239 patients.
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.