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通过精确回忆分析,调查食道SCC和食道的内镜成像中的物体检测错误和失生症

Li-Jen Chang^1,2, Kun-Hua Lee^3,4, Arvind Mukundan^4,5

¹Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, Taiwan.

Frontiers in oncology

|December 22, 2025

概括

对象检测模型通过识别文物改善了食道状细胞癌 (ESCC) 的检测,但在微妙的早期病变方面遇到了困难. 需要进一步的研究,以便在内镜中更好地早期检测疾病.

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:

食道状细胞癌 (ESCC) 的检测在白光内镜 (WLI) 中具有挑战性,原因是微妙的病变和模仿器件.
开发自动检测系统对于提高早期诊断率至关重要.

研究的目的:

在WLI图像上对五种物体检测模型在识别ESCC和相关病理方面的性能进行基准测试.
评估包括文物标签在检测准确性的影响.

主要方法:

五个物体探测器 (YOLOv5,YOLOv8,更快的R-CNN,SSD,RT-DETR) 在WLI数据集上从头开始训练.
使用了两种标签策略:一组4个标签 (SCC,发育不良,出血,炎症) 和一组11个标签,包括文物.
在310图像测试组上,在0.50的IOU下,使用宏精度,回忆和F1分数来评估性能.

主要成果:

包括文物类改进了整体检测指标,其中YOLOv5和YOLOv8表现最好.
然而,用于早期疾病检测的召回,特别是发育不良症和SCC,仍然有限.
混分析表明,错误主要是由于错误检测而不是错误分类.

结论:

标记文物可以减少与非损伤发现的混,但并不能完全解决检测微妙早期ESCC的挑战.
未来的工作应该集中在内镜特定的预训练,人工物抑制技术和外部验证上,以提高早期疾病检测.

关键词:

频段的选择频段的选择深度学习是一种深度学习.这是食道癌的癌症.食管状细胞癌的癌症超光谱成像技术的使用.机器学习是机器学习.

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