Representation learning enables robust single cell phenotyping in whole slide liquid biopsy imaging

Tumor-associated cells in liquid biopsy are promising biomarkers for cancer detection, diagnosis, prognosis, and monitoring. Yet, their rarity, heterogeneity, and plasticity pose challenges for accurate identification and characterization. Enrichment-free whole slide imaging of all circulating cells offers a comprehensive, unbiased approach to capture this phenotypic diversity. However, current analysis methods often rely on engineered features and manual expert review, making them prone to technical variability and subjective bias. To address this, we present a deep contrastive learning framework for feature extraction from whole slide immunofluorescence microscopy images, enabling robust identification and stratification of single circulating cells. Our learned features achieve 92.64% accuracy in classifying diverse cell phenotypes and improve downstream tasks such as outlier detection and clustering. Additionally, our model enables automated identification and enumeration of rare phenotypes, reaching an average F1-score of 0.93 on contrived samples mimicking circulating tumor and endothelial cells, and 0.858 across circulating tumor cell phenotypes in clinical samples. This workflow provides a scalable, reproducible solution for analyzing tumor-associated cellular biomarkers, with strong potential to enhance clinical prognosis and guide personalized treatment strategies.