基于使用专家组合的化和质量诊断算法的分类
Yuma Konaka1, Takuya Ueda1, Ryusei Inamori2
1Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-ku, Sendai, Miyagi, Japan.
PloS one
|September 4, 2025
概括
一个新的深度学习算法MoRFSE模仿放射学家进行改善乳腺癌分类. 这种人工智能方法提高了乳房影像的诊断准确性,显示出临床应用的巨大潜力.
科学领域:
- 医学成像
- 人工智能
- 癌症学
背景情况:
- 精确的乳腺癌分类是及时治疗的关键.
- 深度学习 (DL) 模型是有前途的,但往往缺乏专家放射科医生的细微决策.
- 放射科医生将各种发现,如化和质量进行诊断.
研究的目的:
- 评估用于乳腺癌分类的综合DL算法 - - 特定专家的放射性发现组合 (MoRFSE).
- 评估MoRFSE模拟放射科医生的诊断过程的能力.
- 将MoRFSE的性能与传统的DL模型 (ResNet18) 进行比较.
主要方法:
- 使用TOMPEI-CMMD数据集中的2,764张乳房图像.
- 开发了一个门网 (gNet) 和专业专家网络 (cExp,mExp) 的MoRFSE,用于化和质量.
- 使用5倍交叉验证,并使用DeLong的显著性测试将AUC与ResNet18进行比较.
主要成果:
- 与ResNet18的0. 9577 (p=0. 001348) 相比,MoRFSE的AUC显著更高.
- 在MoRFSE的专业专家网络有效地捕捉了不同的放射性特征.
- 综合方法显示出优异的分类性能.
结论:
- 通过集成专门的DL组件,MoRFSE模型成功地模仿了放射科医生的诊断工作流程.
- 乳腺癌诊断的准确性可以提高.
- 这种人工智能策略为计算机辅助乳腺癌检测提供了有前途的进步.
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