A Unified Deep Learning Framework for Instance Segmentation Across Diverse Cytological Stains

OBJECTIVE: Assess whether a single instance-segmentation model can operate robustly across multiple cytological stains, avoiding stain-specific pipelines without sacrificing accuracy. METHODS: on validation/test to decouple detection tolerance from boundary precision, using identical training and evaluation protocols across models. RESULTS: ). CONCLUSIONS: on the combined dataset, supporting a unified multi-stain, scalable path to AI-assisted cytopathology and practical screening workflows. INSIDE THIS MONTH'S CYTOPATHOLOGY: Can a single computer vision model handle the diversity of cytological stains found in clinical routine? This study validates a unified deep learning approach that effectively segments cells across Papanicolaou, Feulgen and AgNOR protocols. Results suggest that laboratories can deploy simplified, stain-invariant screening tools without compromising diagnostic precision, overcoming a major barrier in digital pathology interoperability.