基于知识驱动和数据驱动的方法对乳房超声波图像病变定位和诊断的研究
Jianqiang Li1, Lintao Song1, Xiaoling Liu1
1School of Computer Science, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, 100124, China.
Computers in biology and medicine
|January 21, 2026
概括
使用超声波早期发现乳腺癌至关重要. 本综述探讨了用于病变定位和诊断的计算机辅助诊断系统,强调了功能构建方法以提高准确性.
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:
- 乳腺癌是对妇女健康的主要威胁;早期检测可以显著降低死亡率.
- 超声波是关键的查工具,由于其可访问性,非侵入性和有效性在密集的乳腺组织.
- 计算机视觉的进步使计算机辅助诊断 (CAD) 能够实现自动化乳腺损伤定位和诊断.
研究的目的:
- 审查最近关于乳房超声波病变定位和诊断的研究.
- 强调模型特征构造在改进CAD系统中的作用.
- 为相关研究提供数据集,指标和选择标准的概述.
主要方法:
- 对乳房超声波病变检测和诊断研究的综合文献分析.
- 现有模型的分类,分为基于知识的,基于数据的和混合的方法.
- 在这些模型中检查特征构造技术.
主要成果:
- 确定了超声波成像方面的挑战,包括斑点噪声,模糊的边界和低对比度,影响了病变检测.
- 审查了用于乳房超声波CAD系统的各种特征构建策略.
- 根据其对特征工程和诊断任务的方法对模型进行分类.
结论:
- 在超声波图像中准确地定位乳腺病变和诊断仍然具有挑战性.
- 在特征构造方面的进步对于提高CAD系统性能至关重要.
- 未来的研究应该专注于克服目前的局限性,以提高诊断准确度.
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