在内镜检查过程中基于提示的多片细分
Xinzhen Ren1, Wenju Zhou1, Naitong Yuan1
1Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, CO 200444, China.
Medical image analysis
|March 12, 2025
概括
这项研究引入了一种基于提示的多细分方法 (PPSM),使用人工智能和内镜眼睛跟踪数据来改善内镜期间的早期癌症诊断. 该PPSM精确地细分了多,提高了诊断能力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 准确的聚合物大小识别对于内镜癌症诊断至关重要.
- 模糊的聚边界导致错误细分和错误诊断.
- 目前的方法在现实世界内镜场景中缺乏效率和准确性.
研究的目的:
- 开发一种基于提示的聚细分方法 (PPSM),以在内镜中改善早期癌症诊断.
- 将内镜师的专业知识与人工智能相结合,以改善多体细分.
- 为实时辅助诊断创建一个强大且可适应的系统.
主要方法:
- 提出了一个基于提示的聚细分网络 (PPSN),具有提示编码,特征提取和掩码解码模块.
- 使用内镜眼睛注意力数据 (视线) 作为指导细分的提示.
- 实现了不统一的点矩阵提示,以确保稳定性和基于SAM的数据增强,以提高适应性.
主要成果:
- 在聚合物细分方面,PPSM表现出高精度和实时性能.
- 在多个数据集上进行交叉培训和交叉测试证实了该方法的概括能力.
- 开发了一次性电子内镜和图像处理器,具有实时辅助诊断功能.
结论:
- PPSM有效地结合了人工智能和人类专业知识,以实现精确的多重体细分.
- 开发的系统显示了在内镜检查期间改善早期癌症检测率的巨大潜力.
- 这项研究有助于开发用于胃肠内镜的先进诊断工具.
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