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
概括
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.
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.
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