通过Wasserstein距离和差异度量对胸部X射线图像分类进行域调整
Bishi He1, Yuanjiao Chen1, Darong Zhu2
1School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, China.
Scientific reports
|February 1, 2024
概括
这项研究介绍了WDDM,这是一种新的域适应方法,用于使用深度学习来诊断胸部放射. 在医学成像分析中,WDDM提高了异常识别准确度和概括性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 深度学习有助于胸部X射线诊断,但传统方法在本地化和特征提取方面扎.
- 医疗成像中现有的域调整技术面临着准确性和模型复杂性的挑战.
研究的目的:
- 开发一种新的域适应方法 (WDDM),以改善胸部放射图像中的异常识别.
- 克服传统方法在病变定位,特征提取和参数效率方面的局限性.
主要方法:
- 拟议的WDDM方法结合了Wasserstein距离和差异测量,用于域调整.
- 使用BiFormer作为一个多尺度的特征提取器,用于增强的深度特征表示.
- 采用了对Wasserstein距离和对比域差异的损失最小化,用于样本选择.
主要成果:
- 与非转移方法相比,WDDM方法显示了14.8%的显著平均AUC增加.
- 在胸部放射图像上实现了更高的分类准确度和优越的概括性能.
- 在提取差异性特征方面,BiFormer的表现优于传统方法.
结论:
- 在胸部X射线分析中,WDDM为域调整提供了强大的解决方案.
- 拟议的方法提高了医疗AI中的诊断准确性和模型通用性.
- WDDM有效地解决了医疗图像解释深度学习的关键挑战.
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