基于Tomek链接的SMOTE方法用于血细胞分类中的类不平衡,具有双路径滑动窗注意力模型
Sonia Mustafa1, Gang Li1, Yasir Iqbal2
1Medical School of Tianjin University, Tianjin, 300072, China; State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China.
Talanta
|February 2, 2026
概括
一种新的双路径滑动窗口注意力 (DP-SWA) 模型通过克服多谱成像 (MSI) 数据集中的类不平衡,提高了对全血计 (CBC) 的医学图像分析,达到99.02%的准确性.
科学领域:
- 医疗图像处理 医学图像处理
- 医疗保健中的人工智能
- 计算病理学计算病理学
背景情况:
- 针对全血计 (CBC) 分析的多光谱成像 (MSI) 数据集经常存在类不平衡,阻碍了深度学习模型的性能.
- 准确的CBC分析对于诊断贫血,感染和神经退行性疾病等疾病至关重要.
研究的目的:
- 开发一个先进的深度学习模型,有效地解决MSI数据集中的类不平衡,以加强CBC分析.
- 引入一种新的双路径滑动窗口注意力 (DP-SWA) 模型,以提高诊断准确性和计算效率.
主要方法:
- 一个新的双路径滑动窗口注意力 (DP-SWA) 模型,包含用于特征提取的卷积块,具有本地和全球滑动窗口注意力的双路径处理,以及融合压缩块.
- 实施混合SMOTE-Tomek Links数据平衡策略,以缓解阶级不平衡.
- 评估模型在多波长MSI数据集上的性能.
主要成果:
- DP-SWA模型实现了99.02%的最先进的精度,明显超过了基线YOLOv8-N (93.01%) 和其他当代模型.
- 该模型表现出令人印象深刻的计算效率,推断时间为4.3毫秒,参数为0.937万,GFLOP为1.05.
- 混合数据平衡策略有效消除了类不平衡问题.
结论:
- DP-SWA模型为MSI数据集中的类不平衡提供了强大的解决方案,从而在CBC分析中实现了卓越的诊断准确性.
- 该模型的高精度和计算效率为通过改进的医疗图像处理来推进临床诊断提供了重大潜力.
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