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CRViT-YOLO:一种使用卷积重组视觉转换器检测多形态血细胞的方法

Yaning Du¹, Yuliang Ma², Qingshan She³

¹School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Jinan Key Laboratory of Rehabilitation and Evaluation of Motor Dysfunction, The People's Hospital of Huaiyin, Jinan, Shandong 250100, China.

|January 13, 2026

概括

这项研究介绍了CRViT-YOLO,这是一种用于准确有效地检测血细胞的深度学习模型. 该框架通过增强特征提取和针对不同细胞类型的定位来显著改善传统方法.

科学领域:

医学诊断医学诊断医学诊断
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

手动全血细胞计数是费时的,容易出现错误.
深度学习为自动化血细胞分析提供了一个有希望的途径.
现有的方法面临着细胞形态,染色和图像质量变化的挑战.

研究的目的:

开发一个先进的深度学习框架,以准确高效地检测血细胞.
为了改善特征提取和血细胞图像中的定位精度.
验证拟议模型在不同血细胞数据集上的性能.

主要方法:

开发了CRViT-YOLO,这是一个基于YOLOv9架构的新型检测框架.
整合了一个卷积重建视觉转换器 (CRViT) 模块,用于增强特征提取.
集成了功能增强模块 (FEM) 和EIoU损失函数,以提高功能表示和本地化准确度.

主要成果:

在四个公共数据集上,CRViT-YOLO实现了高平均精度 (mAP@50) 评分:BCCDD (93.9%),BCDD (99.4%),LISC (98.8%) 和BBBC041 (76.0%).
该模型在检测多态细胞,健康细胞和病态细胞方面表现强.
有效地处理各种规模和类型的密集或重叠的细胞.

结论:

CRViT-YOLO为自动化多类血细胞检测提供了一个高效和可通用的解决方案.
与传统方法相比,该框架显著提高了准确性和效率.
提出的方法显示出临床诊断应用的巨大潜力.

关键词:

血液细胞检测检测血液细胞检测在 CRViT 里面,你会看到 CRViT.对象检测检测对象检测对象检测视觉变压器模型的模型这就是YOLOv9的意思.

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