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使用深度特征融合神经网络进行无标签白细胞分类

Jin Chen¹, Liangzun Fu¹, Maoyu Wei¹

¹Ministry of Education Key Laboratory of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.

|June 7, 2024

概括

一种新的深度学习方法提高了白细胞 (WBC) 的分类,使用特征融合来获得无标签的明亮场图像. 这种方法提高了诊断的准确性,并支持未来的 point-of-care 应用.

科学领域:

生物医学工程生物医学工程
计算生物学计算生物学
医疗成像医学成像

背景情况:

白血细胞 (WBC) 的分类对于疾病诊断至关重要.
像流细胞计这样的传统方法昂贵而复杂.
现有的深度学习方法往往忽略了WBC中微妙的细胞内特征.

研究的目的:

开发一种新的深度学习模型,用于无标签的WBC分类.
通过利用内部细胞结构来提高WBC识别的准确性.
为传统的诊断方法提供简化,经济高效的替代方案.

主要方法:

提出了一个包含特征融合的神经网络.
来自卷积神经网络 (CNN) 层的低层和高层特征的组合.
利用明亮场显微镜图像进行无标签的WBC检测.

主要成果:

在测试数据集上获得了80.3%的准确性.
证明了微妙的细胞内特征的有效利用.
该方法简化了细胞检测,并消除了染色要求.

结论:

拟议的特征融合网络为准确,无标签的WBC分类提供了一个有希望的方法.
这种方法有可能推进基于深度学习的生物医学诊断.
它可以促进小型流动细胞计的开发,用于点的护理诊断.

关键词:

生物医学诊断的诊断是生物医学诊断.功能融合的特点是:没有标签的无标签.神经网络的神经网络在WBC分类中,WBC的分类是:

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