在使用多分支框架和投票合奏的内支部超声波图像中对外周肺病变的分类
Huitao Wang1, Takahiro Nakajima2, Kohei Shikano3
1Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba, 263-8522, Chiba, Japan.
Computers in biology and medicine
|September 18, 2025
概括
一种新型的深度学习计算机辅助诊断 (CAD) 系统用于肺癌,通过使用内支骨超声扫描 (EBUS) 图像改善了早期检测. 该系统有效地处理数据不平衡,并通过多部门框架和图像转换提高诊断准确性.
科学领域:
- 医疗成像医学成像
- 在瘤学中使用人工智能
- 诊断系统 诊断系统
背景情况:
- 肺癌是全球癌症死亡的主要原因之一.
- 早期诊断对于有效治疗肺癌至关重要.
- 胸膜内膜超声波 (EBUS) 是肺癌早期检测的一个关键工具.
研究的目的:
- 为肺癌开发基于深度学习的计算机辅助诊断 (CAD) 系统.
- 为了提高医疗专业人员的诊断精度和效率.
- 为了解决肺癌数据集中的数据不平衡挑战.
主要方法:
- 提出了一个跨行业的深度学习框架来处理数据不平衡.
- 采用多数投票的方式,将多个分支机构的输出整合到最终诊断中.
- 脑内膜超声波 (EBUS) 图像从极地坐标转换为笛卡尔坐标.
主要成果:
- 多分支CAD系统实现了曲线下的面积 (AUC) 为0.80.
- 该系统的精度为0.78和F1得分为0.80.
- 关键性能指标包括0.85的灵敏度和0.72.7的特异性.
结论:
- 多部门框架和集体学习有效地解决了医学诊断中的数据不平衡问题.
- 坐标系统图像转换优化了对深度学习模型的图像结构理解.
- 开发的CAD系统显示了改善肺癌诊断的前景.
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