CG-RecNet: a gated and attention-fused deep learning framework for label-free classification of neural stem cell differentiation via imaging flow cytometry

Introduction: Precise and longitudinal monitoring of Neural Stem Cell (NSC) differentiation is pivotal for advancing regenerative medicine. However, traditional identification methods rely on invasive immunochemical staining, which terminates cell viability and precludes real-time analysis. Methods: To address these limitations, we propose CG-RecNet, a specialized deep learning framework for accurate, label-free classification of NSC differentiation lineages-specifically neurons, astrocytes, and oligodendrocytes-directly from brightfield imaging flow cytometry (IFC) data. The architecture integrates a LinAngular Cross-Channel Attention (LinAngular-XCA) Fusion Module to capture global morphological dependencies and a Gated Convolutional Neural Network (GatedCNN) Block to suppress background noise. Results: Validation on rat embryonic NSCs indicates that CG-RecNet achieves an overall accuracy of 96.40% and a macro-average AUC of 0.9979, representing a 1.82% improvement over established baselines. Notably, the model achieved high precision in identifying the minority oligodendrocyte lineage without synthetic oversampling. Discussion: Grad-CAM analysis indicates that the model's attention aligns with biologically relevant hallmarks, such as neurite outgrowth and soma texture. CG-RecNet provides a reliable, non-invasive, and qualitatively interpretable tool for neural stem cell research.