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Updated: Jan 15, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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基于检测的两阶段框架,用于内部操作的ROSE WSI分类.

Yingjiao Deng1, Qing Zhang1, Chunhua Zhou2

  • 1Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241, China.

Computer methods and programs in biomedicine
|October 8, 2025
PubMed
概括
此摘要是机器生成的。

一个新的两阶段框架增强了胰腺癌幻灯片的快速现场评估 (ROSE). 这种人工智能方法提高了诊断准确度,并大大减少了手术内决策的计算时间.

关键词:
多个实例的学习是多个实例的学习.对象检测检测对象检测对象检测胰腺固体病变的发生整个幻灯片图像分析.

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科学领域:

  • 人工智能在病理学中的应用
  • 数字病理学数字病理学
  • 计算细胞学计算细胞学

背景情况:

  • 固体胰腺病变 (SPLs) 是一种高度致命的胃肠道恶性瘤.
  • 快速现场评估 (ROSE) 对于手术诊断至关重要,但在千兆像素全幻灯片图像 (WSI) 中面临解释挑战.
  • 目前的ROSE解释受到大图像尺度,稀疏的诊断区域以及实时反的需求的阻碍.

研究的目的:

  • 为ROSE全幻灯片图像 (WSI) 分类开发一种新,高效和精确的两阶段框架.
  • 模拟细胞病理学家的临床诊断工作流程,用于ROSE幻灯片解释.
  • 为了加速胰腺癌诊断中的实时手术内决策.

主要方法:

  • 一个两阶段的框架,结合了对象检测和多个实例的学习.
  • 阶段1:RoF DETR,基于变压器的网络,用于在5倍放大时检测细胞群,结合基础模型特征和多尺度融合.
  • 第二阶段:以原型为导向的多实例学习 (PG-MIL) 与伪袋增强用于20倍放大补丁提取,增强区分和稳定性.

主要成果:

  • 在专门的ROSE WSI数据集上,在细胞群检测中实现了0.482 AP@0.5和在WSI级分类中达到92.36%的AUC.
  • 与传统的WSI管道相比,拟议的框架将计算开销减少约100倍.
  • 推断时间缩短了一半,显示出显著的效率提升.

结论:

  • 开发的框架为ROSE片的快速细胞学评估提供了一个可扩展和高效的解决方案.
  • 这种方法有可能在临床环境中显著支持实时的手术内决策.
  • 这种人工智能驱动的方法解决了ROSE解释的关键挑战,为改善胰腺癌诊断铺平了道路.