重新审视现实世界视频结肠镜中的挑战,使用端到端两流多重体检测变压器 (TS-PDTR)
Tianyuan Gan1, Chongan Zhang1, Peng Wang1
1Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38, Zheda Road, 310027, Hangzhou, Zhejiang, China.
Journal of medical systems
|October 8, 2025
概括
本研究介绍了双流聚检测变压器 (TS-PDTR),这是一种用于增强结肠镜视频中的聚检测的新型深度学习模型. TS-PDTR显著提高了准确性,有助于早期结直肠癌的诊断.
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:
- 准确的聚检测对于早期结直肠癌 (CRC) 诊断和治疗至关重要.
- 结肠镜视频对现有的检测算法提出了挑战,因为复杂的现实世界临床条件.
- 视频数据为先进的检测系统提供了比单个图像更丰富的时间和上下文信息.
研究的目的:
- 开发一个端到端的深度学习网络,用于在结肠镜视频中检测息肉.
- 为了提高模型对小和伪装的息肉的敏感性.
- 为了提高视频结肠镜中对图像质量差的稳定性.
主要方法:
- 提出了一个端到端的双流聚合体检测变压器 (TS-PDTR) 网络.
- 利用双流特征提取,从RGB和光学流中获取空间和时间信息.
- 集成的细节感知卷积 (DAConv) 和细节引导注意 (DGA) 模块用于功能增强.
- 采用流融合编码器 (FFE) 来整合时间线索以获得稳定性.
主要成果:
- 在基准数据集上,TS-PDTR的性能超过了基于图像和视频的最先进的息肉检测方法.
- 在LDPolypVideo数据集上获得了33.2的平均平均精度 (mAP).
- 在SUN结肠镜视频数据库上改进了mAP,达到64.0,在CVC-VideoClinicDB上达到55.6.
结论:
- 作为基于视频的多胞体检测方法,TS-PDTR表现出卓越的性能.
- 该模型显示了在改善结直肠癌查方面具有很强的临床应用潜力.
- 进一步开发TS-PDTR可能会显著提升在结肠镜检测中自动检测的多重体.
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