Disentangling Coincident Cell Events Using Deep Transfer Learning and Compressive Sensing

Accurate single‐cell analysis is critical for diagnostics, immunomonitoring, and cell therapy, but coincident events, where multiple cells overlap in a sensing zone, can severely compromise signal fidelity. A hybrid framework combining a fully convolutional neural network (FCN) with compressive sensing (CS) to disentangle such overlapping events in 1D sensor data is presented. The FCN, trained on bead‐derived datasets, accurately estimates coincident event counts and generalizes to immunomagnetically labeled CD4 + and CD14 + cells in whole blood without retraining. Using this count, the CS module reconstructs individual signal components with high fidelity, enabling precise recovery of single‐cell features, including velocity, amplitude, and hydrodynamic diameter. Benchmarking against conventional state‐machine algorithms shows superior performance, recovering up to 21% more events and improving classification accuracy beyond 97%. Explainability via class activation maps and parameterized Gaussian template fitting ensures transparency and clinical interpretability. Demonstrated with magnetic flow cytometry (MFC), the framework is compatible with other waveform‐generating modalities, including impedance cytometry, nanopore, and resistive pulse sensing. This work lays the foundation for next‐generation nonoptical single‐cell sensing platforms that are automated, generalizable, and capable of resolving overlapping events, broadening the utility of cytometry in translational medicine and precision diagnostics, e.g., cell‐interaction studies.