Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning

• URL: http://arxiv.org/abs/2208.10698v1
• Date: Tue, 23 Aug 2022 02:39:08 GMT
• Title: Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning
• Authors: Xin Wei, Huaiwei Cong, Zheng Zhang, Junran Peng, Guoping Chen, Jinpeng Li
• Abstract summary: Long-term vertebral fractures severely affect the life quality of patients, causing kyphotic, lumbar deformity and even paralysis. In particular, the mild fractures and normal controls are quite difficult to distinguish for deep learning models and inexperienced doctors. Motivated by this, we propose a supervised contrastive learning based model to estimate Genent's Grade of vertebral fracture with CT scans. Our method has a specificity of 99% and a sensitivity of 85% in binary classification, and a macio-F1 of 77% in multi-classification, indicating that contrastive learning significantly improves the accuracy of vertebrae fracture screening.
• Score: 11.944282446506396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-term vertebral fractures severely affect the life quality of patients, causing kyphotic, lumbar deformity and even paralysis. Computed tomography (CT) is a common clinical examination to screen for this disease at early stages. However, the faint radiological appearances and unspecific symptoms lead to a high risk of missed diagnosis. In particular, the mild fractures and normal controls are quite difficult to distinguish for deep learning models and inexperienced doctors. In this paper, we argue that reinforcing the faint fracture features to encourage the inter-class separability is the key to improving the accuracy. Motivated by this, we propose a supervised contrastive learning based model to estimate Genent's Grade of vertebral fracture with CT scans. The supervised contrastive learning, as an auxiliary task, narrows the distance of features within the same class while pushing others away, which enhances the model's capability of capturing subtle features of vertebral fractures. Considering the lack of datasets in this field, we construct a database including 208 samples annotated by experienced radiologists. Our method has a specificity of 99\% and a sensitivity of 85\% in binary classification, and a macio-F1 of 77\% in multi-classification, indicating that contrastive learning significantly improves the accuracy of vertebrae fracture screening, especially for the mild fractures and normal controls. Our desensitized data and codes will be made publicly available for the community.

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