改进深度学习模型，提升流式细胞术中微小残留病检测精度
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data
- 结合局部与全局特征学习，优化模型对复杂数据的捕捉能力
- 在公开数据集上性能超越现有最佳模型，跨实验室泛化能力更强
- 为流式细胞术数据分析的模型设计提供实用指导，适合生物医学研究者
本文评估了多种深度学习方法在流式细胞术（FCM）数据中可测量微小残留病（MRD）检测中的表现，探讨了建模长程依赖关系、获取全局信息的方法以及学习局部特征的重要性。基于研究发现，我们对当前最优模型（SOTA）提出两项改进。贡献包括一个增强版SOTA模型，在公开数据集上表现更优，且在不同实验室间具备更强的泛化能力；同时为FCM领域提供了有价值的见解，有助于未来深度学习架构的设计。代码已开源：https://github.com/lisaweijler/flowNetworks。
原文摘要 · Abstract (English)
This paper evaluates various deep learning methods for measurable residual disease (MRD) detection in flow cytometry (FCM) data, addressing questions regarding the benefits of modeling long-range dependencies, methods of obtaining global information, and the importance of learning local features. Based on our findings, we propose two adaptations to the current state-of-the-art (SOTA) model. Our contributions include an enhanced SOTA model, demonstrating superior performance on publicly available datasets and improved generalization across laboratories, as well as valuable insights for the FCM community, guiding future DL architecture designs for FCM data analysis. The code is available at \url{https://github.com/lisaweijler/flowNetworks}.
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