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细胞流:从空间视图到时间轨迹的病态图像增强的进步

Zeyu Liu¹, Tianyi Zhang², Yufang He¹

¹School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.

Medical image analysis

|February 18, 2026

概括

细胞流,一种新的时间意识生成框架,通过建模疾病进展来增强病理图像分析. 这种方法提高了诊断准确度和数据增强,用于计算病理学任务.

科学领域:

计算病理学计算病理学
医学图像分析医学图像分析
人工智能的人工智能

背景情况:

病理学中的深度学习受到细粒度任务的稀缺注释数据的限制.
现有的空间数据增强方法缺乏形态合理性和时间意识.

研究的目的:

介绍Cellflow,这是第一个用于病理图像增强的时间意识生成框架.
将病理过渡模型作为生物学上可信的时间轨迹.

主要方法:

细胞流使用基于阶梯的扩散桥,带有分类器引导的概率流 ODE.
产生中间状态,捕获细胞和组织水平的细节.

主要成果:

细胞流在7个不同的数据集中表现优于空间增强和生成模型.
实现了更好的分类性能,图像保真性和时间连贯性.
定量分析验证了生成的过渡序列的生物真实性.

结论:

细胞流代表了病理数据增强中的范式转变,从空间模型转向时间模型.
能够进行强大的模型训练,罕见疾病探索和计算病理学的教育模拟.

关键词:

数据增强数据增强生成性扩散模型是一种生成性扩散模型.病理学图像分析图像分析时间建模时间建模

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