这篇论文用深度学习从微流体轨迹里识别癌细胞表型，提出了一个叫SubSeq的增强方法，比基线更准，还告诉你模型在看哪些物理区域。
该论文提出一种可解释且数据高效的深度神经网络框架，用于基于轨迹的循环肿瘤细胞（CTC）表型分类。为解决轨迹数据稀缺问题，作者开发了SubSeq靶向增强策略，在训练时随机提取局部轨迹片段，促进模型从局部模式中学习。实验表明，SubSeq相比基线和其他增强方法提升了分类准确率。梯度加权类激活映射分析显示，局部轨迹片段包含大量与准确分类相关的生物物理信息，同时表明全长轨迹存在冗余。该框架将微流体几何视为细胞力学特性的物理编码器，为诊断设备设计提供机制性见解。
Data-Efficient and Interpretable Classification of Circulating Tumor Cell Phenotypes in Microfluidic Devices via Deep Learning
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.