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评估类别不平衡的骨髓细胞分类模型的实际应用

Zhigang Hu¹, Aoru Ge¹, Xinzheng Wang¹

¹School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.

|January 30, 2025

概括

开发一种用于急性髓性白血病 (AML) 细胞分类的自动化模型至关重要. 这项研究推使用RegNetX-3.2gf模型,以成本敏感损失和SE为准确和高效的髓状细胞分析.

科学领域:

血液学血液学血液学
计算生物学计算生物学
医疗成像医学成像

背景情况:

急性髓性白血病 (AML) 发病率正在上升,需要改进诊断方法.
目前用于AML细胞分类的手动显微镜是主观和低效的.
需要自动化骨髓细胞分类模型来提高诊断的准确性和速度.

研究的目的:

评估和优化AML诊断中骨髓细胞的自动分类模型.
为了应对骨髓数据集中细胞类型分布不平衡的挑战.
确定最佳的损失函数和注意力机制,以提高模型性能.

主要方法:

在大型骨髓细胞数据集上评估了五种分类模型.
分析了五个损失函数和七个注意力机制,以减轻数据不平衡.
使用Swin变压器V2和RegNetX-3.2gf模型进行性能比较.
嵌入式成本敏感损失 (CS) 和挤压激发 (SE) 网络.

主要成果:

斯温变压器V2表现出强的性能,但RegNetX-3.2gf提供了更好的平衡较少的参数和更快的推理.
将RegNetX-3.2gf与成本敏感损失和SE关注 (RegNetX-3.2gf + CS + SE) 结合起来,取得了卓越的结果.
最佳模型的平均精度为68.183%,回忆率为63.722%,F1得分为65.155%,显著改善了基线性能.
类激活地图证实了该模型的重点是相关的细胞特征,特别是细胞核.

结论:

RegNetX-3.2gf是一种实用且高效的模型,用于AML中自动化骨髓状细胞分类.
综合成本敏感损失和SE注意力有效处理数据不平衡并提高分类准确性.
这种优化的模型代表了智能AML诊断和分类的重大进步.

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