一个基于双流特征融合和双域关注的轻量级网络,用于白细胞细分
Yang Luo1, Yingwei Wang1, Yongda Zhao1
1School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning, China.
Frontiers in oncology
|September 21, 2023
概括
这项研究引入了一种新的,轻量级的网络,用于在细胞病理图像中实现自动化白细胞细分,实现高精度,同时降低计算成本. 该方法提高了白血病评估的诊断效率.
科学领域:
- 医疗成像医学成像
- 计算病理学计算病理学
- 人工智能在医学中的应用
背景情况:
- 精确的白细胞细分对于从细胞病理图像中诊断出白血病至关重要.
- 目前的手动和自动化方法面临的挑战包括细胞变形,模糊的边界和重叠的细胞,导致耗时和容易出错的诊断.
研究的目的:
- 开发一种高精度和计算效率的自动化方法来对白细胞进行细分.
- 改进特征表示和信息获取,以提高细胞病理学中的细分性能.
主要方法:
- 使用Ghost模块重建一个轻量级的骨干网络,以重复使用功能,减少参数和冗余.
- 采用双流特征融合网络 (DFFN) 和双域注意模块 (DDAM) 来增强详细信息和全局特征提取.
主要成果:
- 拟议的方法在ALL-IDB和BCCD数据集上实现了87.41%的平均精度 (AP),超过了一些现有的实例细分网络.
- 与最先进的单阶段方法相比,该方法显示了参数和计算成本的显著降低.
结论:
- 开发的方法为准确和高效的白细胞细分提供了有希望的解决方案,有助于白血病评估.
- 未来的工作将专注于进一步优化网络,以提高准确性和更轻的模型.
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