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半监督的乳腺损伤细分使用信心排名的特征和双级原型.

Siyao Jiang, Huisi Wu, Yu Zhou

    IEEE transactions on neural networks and learning systems
    |October 9, 2025
    PubMed
    概括

    这项研究介绍了CoBiNet,这是一个新的半监督框架,用于对乳房超声波图像进行细分. 它通过对特征进行排名和使用双层原型来提高准确性,解决计算机辅助诊断方面的挑战.

    科学领域:

    • 医疗成像医学成像
    • 计算机辅助诊断 计算机辅助诊断
    • 人工智能在医学中的应用

    背景情况:

    • 超声波 (BUS) 图像中的自动化乳腺病变细分对于计算机辅助诊断至关重要.
    • 挑战包括繁的数据注释,模两可的损伤边界和BUS图像中的低对比度.

    研究的目的:

    • 提出一个新的半监督乳腺细分框架,CoBiNet,以克服当前的细分挑战.
    • 为了提高自动化乳腺病变细分的准确性和效率.

    主要方法:

    • 开发了一个半监督的框架 (CoBiNet),利用可信度排名的特征和双层原型.
    • 采用双分支架构 (分类器和投影器),具有多层次的特征排名.
    • 实现了跨信任级别 (TCL) 的对比学习和交叉指导 (CG) 的一致性学习.

    主要成果:

    • 在BUSI和UDIAT数据集上,CoBiNet在最先进的方法上表现出优越的性能.
    • 该框架有效地处理了BUS图像中的模两可的界限和低对比度.
    • 通过特征信心排名和对比学习,更好地识别模两可的区域.

    结论:

    • CoBiNet为半监督的乳房超声波图像细分提供了强大而有效的解决方案.

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  • 拟议的方法在乳腺成像中显著推进了计算机辅助诊断.
  • 未来的工作将涉及发布更广泛的研究应用的代码.