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结构信息引导级联特征融合网络用于胃肠道解剖学分类
Xuli Tan1, Xun Gong2, Lin Fan1
1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Xi'an Road, Chengdu, 611756, Sichuan, China.
Annals of biomedical engineering
|November 28, 2025
概括
一个新的深度学习模型,结构信息引导级联特征融合网络 (SIG-CFFNet),准确地识别了胃肠道内镜结构. 这种先进的网络提高了诊断准确度,即使是低质量的图像,也显示出显著的临床潜力.
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:
- 准确识别胃肠道内镜解剖结构对于诊断至关重要.
- 内镜图像质量问题和形态相似性阻碍了准确的识别.
- 现有的方法在低质量和复杂的内镜图像方面扎.
研究的目的:
- 开发一种先进的深度学习模型,用于精确识别胃肠道内镜解剖结构.
- 提高诊断准确度,减少内镜中错过检测率.
- 为了应对低质量的图像和复杂的解剖变异带来的挑战.
主要方法:
- 提出了一个结构信息引导的级联特征融合网络 (SIG-CFFNet).
- 综合卷积神经网络 (CNN) 和变压器功能使用解剖学先前知识.
- 采用深度超参数化的卷积层 (DO-Conv) 来提高特征表示和效率.
主要成果:
- 在正常结构中达到73.47%的准确性,在病态结构中达到87.05%的准确性.
- 在Kvasir-Capsule上达到99.61%的准确性,在HyperKvasir数据集上达到87.83%.
- 证明了强大的跨领域性能 (84.46%COVID19-CT,80.21%ISIC2018) 具有竞争力的召回率.
结论:
- SIG-CFFNet显著改善了胃肠内镜解剖结构的识别.
- 该模型在各种数据集和具有挑战性的条件中显示出高精度和稳定性.
- 拟议的网络具有强大的临床适用性和对现实世界内镜诊断的潜力.
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