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B2Q-Net:用于手术阶段识别的双向分支查询网络.

Wenjie Zhang, Zhiheng Li, Yue Bi

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    |January 16, 2026
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    概括

    双向分支查询网络 (B2Q-Net) 通过整合历史和局部时间信息来改善手术阶段的识别. 这种新的方法提高了实时手术指导的准确性和速度.

    科学领域:

    • 计算机视觉 计算机视觉
    • 医疗成像医学成像
    • 外科手术工作流程分析

    背景情况:

    • 手术阶段识别 (SPR) 对于分析手术工作流程和提供实时指导至关重要.
    • 当前的SPR方法经常汇总框架级数据,限制了将历史背景整合到局部时间建模中.

    研究的目的:

    • 引入一个新的网络,双向分支查询网络 (B2Q-Net),以改进外科手术阶段识别.
    • 通过实现阶段级和框架级特征之间的双向信息流,解决现有方法的局限性.

    主要方法:

    • B2Q-Net将SPR重新定义为阶段级和框架级特征之间的双向查询.
    • 它在阶段查询初始化过程中包含历史信息,并使用双级选择器 (DSS) 进行高质量的阶段查询.
    • 一个带有可学习令牌的状态空间查询 (SSQ) 模块保存历史信息.

    主要成果:

    • 与最先进的方法相比,B2Q-Net在三个数据集中显示出更高的识别准确性.
    • 该网络实现了每秒106 (fps) 的高推断速度.

    结论:

    • 通过有效地整合历史背景,B2Q-Net在手术阶段识别方面取得了重大进展.

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  • 该方法为手术程序提供准确有效的实时指导.