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Updated: Feb 14, 2026

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分段导向预处理提高了深度学习的诊断准确度和信任度的氨基母细胞瘤和骨质生囊在形光束CT图像-A初步研究初步研究

Xinyue Zhang¹, Yuxuan Yang², Chen Zhong²

¹Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, No. 22, Zhongguancun South Avenue, Haidian District, Beijing 100081, China.

Diagnostics (Basel, Switzerland)

|February 13, 2026

概括

分段导向的预处理改善了深度学习,用于区分乳腺母细胞瘤和口腔瘤. 这种方法提高了计算机辅助下巴病变诊断的诊断准确性和解释性.

科学领域:

口腔和面放射学口腔和面放射学
人工智能在医学中的应用
医学图像分析医学图像分析

背景情况:

在治疗规划过程中,准确地分辨艾美洛母细胞瘤和牙生囊瘤之间的诊断至关重要.
深度学习 (DL) 对诊断下病变有希望,但缺乏临床解释性.
分段导向预处理可以提高DL系统的临床实用性.

研究的目的:

引入和评估以细分为导向的预处理技术,用于基于DL的乳腺母细胞瘤和牙产生的角囊瘤的诊断.
提高病变的计算机辅助诊断系统的可解释性和临床相关性.

主要方法:

一个InceptionV3模型在128个CBCT扫描 (64个乳腺母细胞瘤,64个牙细胞瘤) 上使用5倍交叉验证进行了训练和验证.
我们比较了四种输入策略:原始切片,边界盒ROI,精确的细分ROI和适度扩展的ROI.
用准确性,回忆力,精度,F1得分,AUC,Grad-CAM可视化和信心曲线分析来评估模型性能.

主要成果:

在所有指标中,以细分为指导的ROI显著超过了原始切片输入.
适度扩展的ROI实现了最好的整体表现.
边界盒ROI提供了具有更高回忆力的竞争性结果,Grad-CAM证实了改进的注意力本地化.

结论:

分段导向预处理是一种有效的策略,可以增强基于DL的下病变诊断.
这种方法提高了诊断性能和模型解释性,促进了临床实施.
这些发现支持将这种方法集成到口腔病理学计算机辅助诊断系统中.

关键词:

艾梅洛母细胞瘤 (Ameloblastoma) 是一种圆光束CT CT 的情况.可以解释的人工智能AI 医疗图像预处理医学图像预处理牙病原性角质囊细分化细分化的细分化

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