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Updated: Feb 5, 2026

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基于深度学习的自动化管道,用于在增强对比度的CT图像中检测结直肠癌
Chenhui Qiu1, Sarah Miller2, Barathi Subramanian3
1Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
概括
这项研究引入了一个AI管道,用于在CT扫描中检测结直肠癌 (CRC). 深度学习模型显著改善了早期检测和定位,有助于临床决策.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 结肠直肠癌 (CRC) 是一个主要的全球健康问题,在新诊断中排名第三,是癌症死亡的主要原因.
- 准确和早期发现初级CRC对于有效的分期,预后和治疗计划至关重要.
- 目前的检测方法可以通过自动化工具来改进,以提高效率和准确性.
研究的目的:
- 开发和评估基于深度学习的自动检测管道,用于在门静脉相对比增强CT扫描中识别和定位初级结直肠癌 (CRC).
- 通过结合ResNet50模块和一个新的规模适应性损失函数来提高CRC检测的性能.
- 评估管道对内部和外部公共数据集的有效性,并将其与现有方法进行比较.
主要方法:
- 使用YOLOv11作为基线架构开发了一个深度学习管道.
- 一个ResNet50模块被集成到YOLOv11骨干中,以改善特征提取.
- 设计了一种新的尺度适应性损失函数,通过适应性测量交叉点与联盟 (IoU) 和中心点距离来提高界限框回归精度.
主要成果:
- 该管道在内部数据集上实现了高性能,患者级F-1分数为0.8139,切片级F-1分数为0.9921.
- 在外部公开数据集上,管道表现出强烈的概括性,在患者级数据上训练时,F-1得分为0.8348.
- 该方法显著优于现有的检测技术,显示检测灵敏度 (回忆) 比公开数据集中最接近的竞争对手提高了31.97%.
结论:
- 拟议的深度学习管道在CT扫描中检测和定位初级结直肠癌方面表现出卓越的性能.
- 集成ResNet50和规模适应性损失函数显著提高了检测准确性和稳定性.
- 该管道显示了在CRC管理中临床决策支持的巨大潜力,需要在更大的患者队伍中进一步验证.
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