基于深度学习的微流控设备中循环肿瘤细胞表型的高效数据与可解释性分类

中文摘要 AI 辅助

循环肿瘤细胞（CTC）表型的准确分类可为评估转移潜能提供有价值的信息。无标记微流控设备提供了流体力学障碍通道，可将CTC的细微生物物理特征（包括大小和变形性）转化为不同的运动轨迹。然而，控制这些轨迹的高度非线性流体-结构相互作用使得从轨迹数据推断细胞表型的逆问题在分析上难以处理。深度神经网络（DNN）已成为解决该逆问题的强大方法，但其有效性受到轨迹数据有限可用性和缺乏物理可解释性的限制。为应对这些挑战，我们提出了一种用于基于轨迹的CTC分类的可解释且数据高效的DNN框架。为缓解数据稀缺问题，我们开发了Subsequence（SubSeq），一种针对性的增强策略，在训练期间随机提取信息丰富的局部轨迹片段，以促进从局部模式中学习。我们进一步应用Gradient Weighted Class Activation Mapping来识别驱动模型预测的轨迹特征和微流控设备的物理区域。实验结果表明，与评估的基线和增强方法相比，SubSeq提高了分类准确率。此外，可解释性分析表明，局部轨迹片段包含与准确分类相关的大量生物物理信息，这为SubSeq提供了依据，也突出了全长轨迹的冗余性。更广泛地说，所提出的框架将微流控几何结构视为细胞机械特性的物理编码器，提供了可能为诊断设备未来设计提供信息的机制见解。

英文摘要

Accurate classification of circulating tumor cell (CTC) phenotypes can provide valuable information for assessing metastatic potential. Label free microfluidic devices provide a hydrodynamic obstacle course that transforms subtle biophysical characteristics of CTCs, including size and deformability, into distinct kinematic trajectories. However, the highly nonlinear fluid structure interactions governing these trajectories make the inverse problem of inferring cellular phenotype from trajectory data analytically intractable. While deep neural networks (DNNs) have emerged as a powerful approach for addressing this inverse problem, their effectiveness is constrained by the limited availability of trajectory data and the lack of physical interpretability. To address these challenges, we propose an interpretable and data efficient DNN framework for trajectory based CTC classification. To mitigate the scarcity of data, we develop Subsequence (SubSeq), a targeted augmentation strategy that randomly extracts informative local trajectory segments during training to promote learning from localized patterns. We further apply Gradient Weighted Class Activation Mapping to identify the trajectory features and physical regions of the microfluidic device that drive model predictions. Experimental results demonstrate that SubSeq improves classification accuracy over the evaluated baseline and augmentation methods. Furthermore, interpretability analysis suggests that localized trajectory segments contain substantial biophysical information relevant to accurate classification. This provides justification for SubSeq and also highlights the redundancy of full-length trajectories. More broadly, the proposed framework views microfluidic geometries as physical encoders of cellular mechanical properties, providing mechanistic insights that may inform the future design of diagnostic devices.

发表机构

• Texas Tech University（德克萨斯理工大学）

机构由 AI 辅助整理，请以论文原文为准。