域增量白细胞分类与隐私意识的持续学习.
Pratibha Kumari1, Afshin Bozorgpour2, Daniel Reisenbüchler2
1University of Regensburg, Regensburg, 93053, Germany. pratibha.kumari@ur.de.
Scientific reports
|July 15, 2025
概括
本研究介绍了一种使用生成重复的持续学习策略,以防止白细胞 (WBC) 分类模型中的遗忘. 该方法在各种医学成像领域保持高性能,这对于临床诊断至关重要.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 白血细胞 (WBC) 的分类对于诊断血液病症至关重要.
- 医学成像领域的转变 (例如,样本来源,成像条件) 挑战了深度学习模型.
- 传统模型遭受了灾难性的遗忘,基础模型随着分配转移而退化.
研究的目的:
- 制定持续学习 (CL) 策略,以防止WBC分类的基础模型中的遗忘.
- 在动态的临床环境中解决因域移动引起的性能退化问题.
- 为了实现保护隐私的数据重播,以便进行强大的模型训练.
主要方法:
- 为WBC分类提出了一个基于重复的CL策略.
- 利用轻量级的发电机来创建过去数据的合成隐性表示.
- 使用四个数据集和四个骨干模型 (ResNet50,RetCCL,CTransPath,UNI) 进行了广泛的实验.
主要成果:
- 传统的微调方法在先前的任务中表现下降,并且在域移动方面遇到了困难.
- 拟议的CL战略有效地缓解了灾难性的遗忘.
- 模型性能在不同的数据域和任务顺序中得到保留.
结论:
- 基于生成重复的CL策略为保持可靠的WBC分类提供了一个实际的解决方案.
- 这种方法在现实世界的临床环境中增强了模型的稳定性,随着数据分布的不断变化.
- 该方法确保了持续的诊断准确性,尽管领域变化和持续学习.
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