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在视觉模型中使用空间注意力机制来识别和可视化APL中的融合基因亚型

Peirou Yan¹, Guo Pu², Ping Wu³

¹Department of Hematology, Aerospace Center Hospital, Beijing, China.

Frontiers in oncology

|August 14, 2025

概括

一个新的CNN模型使用空间注意力准确地识别了急性肌肉细胞白血病 (APL) 的亚型,从而改善了诊断. 这种方法为临床价值提供了高精度和像素级可视化.

科学领域:

血液学血液学血液学
医疗成像医学成像
人工智能的人工智能

背景情况:

急性促细胞白血病 (APL) 的特点是分化停止,通常是由PML-RARA融合基因引起的.
变异RARα转位可能导致误诊和延迟遗传测试,由于不同的生物特征和治疗反应.

研究的目的:

为APL融合基因亚型开发一个高精度的诊断工具.
为了使骨髓涂抹图像中的关键区域能够在像素级上可视化,用于APL诊断.

主要方法:

开发了一个卷积神经网络 (CNN),集成ResNet块和空间注意模块.
使用了五个类别的骨髓涂抹图像数据集 (PML-RARA,TTMV-RARA,NPM1-RARA,STAT5B-RARA,NUP98-RARG).

主要成果:

在分类APL亚型方面,CNN模型实现了98.04%的整体准确性.
该模型在所有五个类别中都表现出强的表现.
注意地图为模型的预测提供了更好的解释性.

结论:

这种新的方法为诊断APL亚型提供了一种快速且高度准确的方法.
像素级可视化功能对APL诊断和管理具有重要的临床价值.

关键词:

患有急性促细胞白血病.骨髓涂抹图像骨髓涂抹图像骨髓涂抹图像融合基因亚型融合基因亚型病理学的病理学空间注意力机制空间注意力机制

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