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High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging
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在内镜检查期间使用人类视觉转导机制启发的自适应增强网络.

Xinzhen Ren, Wenju Zhou, Maoyu Jin

    IEEE journal of biomedical and health informatics
    |August 1, 2025
    PubMed
    概括

    适应性增强网络 (AEN) 通过恢复损伤细节来提高一次性内镜图像质量. 这种由人工智能驱动的工具提高了诊断准确度和视觉清晰度,以便在内镜检查期间更好地检测腺瘤.

    科学领域:

    • 医学成像医学成像
    • 人工智能的人工智能是人工智能.
    • 内镜检查是指内镜检查.

    背景情况:

    • 一次性内镜可以减少交叉污染,但由于传感器较小,其分辨率较低.
    • 一次性内镜的较低分辨率降低了主观和自动化分析的诊断性能.
    • 改善腺瘤检测率与更清晰的内镜成像有关.

    研究的目的:

    • 提出一个自适应增强网络 (AEN),以增强一次性内镜图像中的病变细节.
    • 通过恢复图像分辨率和清晰度来提高一次性内镜的诊断性能.
    • 为了提高医疗成像,利用受人类视觉机制启发的人工智能.

    主要方法:

    • 开发了一个适应性增强网络 (AEN),模仿人类视觉路径.
    • 集成的棒细胞模块 (RCM) 用于形状敏感的细分和圆细胞模块 (CCM) 用于面向细节的增强.
    • 使用类似于光受体层,外状层,内核层,内状层和状细胞层的框架.

    主要成果:

    • AEN有效地增强了损伤区域,产生了更利的边缘和更细的纹理,减少了模糊和文物.
    • 实验结果显示,用于病变检测的图像质量得到了显著改善.
    • 在四个数据集中进行的全面评估证实了AEN的通用性和有效性.

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    结论:

    • AEN成功地恢复了低分辨率一次性内镜图像中的细节,帮助内镜师.
    • 拟议的人工智能网络为内镜诊断提供实时处理优势.
    • 这项技术有可能提高使用一次性内镜的诊断准确性和患者的治疗结果.