弱监督的核细分与定点引导的注意力和自我监督的伪标签
Yapeng Mo1, Lijiang Chen1, Lingfeng Zhang1
1Institute of Electronic Information Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China.
Bioengineering (Basel, Switzerland)
|January 24, 2025
概括
这项研究引入了一种新的方法,用于精确的核细分,使用中心点预测和伪标签更新. 该方法有效地将相邻的核分离,并减少噪声,以改善临床应用中的自动化分析.
科学领域:
- 医疗成像医学成像
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 手动的核细分是劳动密集型的.
- 弱监督的方法面临着邻近的核和杂的伪标签的挑战.
研究的目的:
- 使用点监督开发精确的核细分方法.
- 为了应对细分邻近核和减轻伪标签噪声的挑战.
主要方法:
- 一个高斯核机制,用于多尺度的中心点预测,以分离相邻的核.
- 一个以点为导向的注意力机制,以减少伪标签的噪音.
- 一个指数移动平均 (EMA) 和k-means基于集群的标签更新机制来提高伪标签质量.
主要成果:
- 在跨多个指标的三个公共数据集上实现了最先进的性能.
- 证明了相邻细胞核的有效分离.
- 显著减少了来自不准确伪标签的噪音的影响.
结论:
- 拟议的方法提供了精确的核细分,并降低了注释成本.
- 促进大规模数据集培训,并促进临床环境中的自动化分析.
- 减少对临床专家的依赖,以完成细分任务.
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