Spatial Regularized Classification Network for Spinal Dislocation Diagnosis

Lai, Bolin; Peng, Shiqi; Yao, Guangyu; Zhang, Ya; Zhang, Xiaoyun; Wang, Yanfeng; Zhao, Hui

doi:10.1007/978-3-030-32692-0_2

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methods that belong to the second category focus on utilizing expert knowledge. Lai et al [24] utilize the expert knowledge that the spinal edges are important for dislocation diagnosis and they use the knowledge to guide model training by introducing a spatial regularization term. In this work, we combine these two categories.…”

Section: B Fine-grained Visual Classification In Medical Imagementioning

confidence: 99%

Two-Stream Compare and Contrast Network for Vertebral Compression Fracture Diagnosis

Feng

Liu

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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 intraclass 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.

show abstract

Section: B Fine-grained Visual Classification In Medical Imagementioning

confidence: 99%