Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning

TL;DR

This paper introduces a deep learning framework that accurately classifies white blood cells and estimates protein expression from label-free microscopy images, eliminating the need for fluorescent labels.

Contribution

It presents a novel hybrid neural network architecture combining convolutional and transformer features, along with an LLM for interpretability, for simultaneous phenotyping and biomarker regression.

Findings

Achieved 91.3% accuracy in WBC classification.

Attained 0.72 Pearson correlation for protein expression regression.

Demonstrated effectiveness on BSCCM and Blood Cells Image benchmarks.

Abstract

Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directly from bright-field morphology remains challenging. We present a unified Deep Learning (DL) framework that jointly performs White Blood Cell (WBC) classification and continuous protein-expression regression from label-free Differential Phase Contrast (DPC) images. Our model employs a Hybrid architecture that fuses convolutional fine-grained texture features with transformer-based global representations through a learnable cross-branch gating module, enabling robust morpho-molecular inference from DPC images. To support downstream interpretability, we further incorporate a Large Language Model (LLM) that generates concise, biologically grounded summaries of the predicted cell states. Experiments on the Berkeley Single Cell Computational Microscopy (BSCCM) and Blood Cells Image benchmarks demonstrate strong performance, achieving a 91.3% WBC classification accuracy and a 0.72 Pearson correlation for CD16 expression regression on BSCCM. These results underscore the promise of label-free single-cell imaging for cost-effective hematological profiling, enabling simultaneous phenotype identification and quantitative biomarker estimation without fluorescent staining. The source code is available at https://github.com/saqibnaziir/Single-Cell-Phenotyping.