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    • Cross-domain self-supervised representation learning for medical image anomaly detection

    • A new breakthrough has been made in the field of medical image anomaly detection, and relevant experts have constructed a cross domain self supervised representation learning framework, effectively solving the problem of semantic differences in pre trained models in medical images, significantly improving the accuracy and robustness of anomaly detection, and providing a reliable solution for the development of this field.
    • Pages: 1-16(2026)   

      Received:26 November 2025

      Revised:2026-02-12

      Accepted:06 March 2026

      Online First:06 March 2026

    • DOI: 10.11834/jig.250599     

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  • ZHAO Yingcheng, Zhu Ning, SONG Xiaogang, HEI Xinhong, SHI Zhenghao. Cross-domain self-supervised representation learning for medical image anomaly detection[J/OL]. Journal of Image and Graphics, 2026:1-16. DOI: 10.11834/jig.250599. DOI:
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相关作者

Liang Jiafei 南方医科大学生物医学工程学院;南方医科大学广东省医学图像处理重点实验室;南方医科大学广东省医学成像与诊断技术工程实验室
Li Ting 南方医科大学护理学院
Yang Jiaqi 西北工业大学计算机学院
Li Yanan 武汉工程大学计算机科学与工程学院、人工智能学院;武汉工程大学智能机器人湖北省重点实验室
Fang Zhiwen 南方医科大学生物医学工程学院;南方医科大学广东省医学图像处理重点实验室;南方医科大学广东省医学成像与诊断技术工程实验室
Yang Feng 南方医科大学生物医学工程学院;南方医科大学广东省医学图像处理重点实验室;南方医科大学广东省医学成像与诊断技术工程实验室
Chen Li 长沙理工大学
Hui Zhang 长沙理工大学;湖南大学

相关机构

School of Biomedical Engineering, Southern Medical University
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University
Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University
School of Nursing, Southern Medical University
School of Computer Science, Northwest Polytechnic University
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