A Robust Shape-Aware Rib Fracture Detection and Segmentation Framework With Contrastive Learning

Author:

Cao Zheng1ORCID,Xu Liming1,Chen Danny Z.2ORCID,Gao Honghao3ORCID,Wu Jian4ORCID

Affiliation:

1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

2. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

3. School of Computer Engineering and Science, Shanghai University, Shanghai, China

4. Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Zhejiang public welfare technology research Project

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Media Technology,Signal Processing

Reference51 articles.

1. Multi-scale Segmentation Network for Rib Fracture Classification from CT Images

2. Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet

3. Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-Rays

4. A simple framework for contrastive learning of visual representations;chen;Int Conf Mach Learn,0

5. U-Net: Convolutional networks for biomedical image segmentation;ronneberger;Proc Int Conf Med Image Comput Comput - Assist Interv,0

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