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混合乳腺超声波数据的领域知识引导的对抗性自适应融合
Xun Gong1, Shuai Yuan2, Yang Xiang2
1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, China; Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Chengdu, 610031, Sichuan, China.
Computers in biology and medicine
|July 20, 2023
概括
这项研究引入了用于乳腺癌诊断的AI框架,使用对比增强超声波 (CEUS),B模式超声波和非成像临床数据. 这种新的方法获得了87.73%的准确性,超过单一模式的方法.
科学领域:
- 医疗成像医学成像
- 在瘤学中使用人工智能
- 生物医学工程 生物医学工程
背景情况:
- 与对比度增强的超声波 (CEUS) 提供了详细的瘤微血管信息,但由于独特的视频特征,它提出了分析挑战.
- 现有的自动化方法往往忽略了来自CEUS和非成像临床 (NIC) 数据的关键时间强度曲线 (TIC) 数据.
研究的目的:
- 开发一种用于乳腺癌诊断的新型人工智能框架,整合CEUS,B模式超声波和NIC数据.
- 为了提高诊断准确性,利用这些混合数据模式的互补性和相关性.
主要方法:
- 采用对抗性自适应融合方法将混合模式数据结合起来.
- 该框架提取CEUS TIC参数,使用框架选策略,并使用关键框架注意网络进行时空特征提取.
主要成果:
- 拟议的AI系统在554名患者的数据集上实现了87.73%的准确性.
- 这种性能大约比单调方法高5%,证明了混合数据融合的有效性.
结论:
- 这是第一个集CEUS TIC参数,NIC数据和超声波成像用于诊断的AI系统.
- 该框架显示了通过多模式数据分析增强自动化乳腺癌诊断的巨大潜力.
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