ArticleCytopathology : official journal of the British Society for Clinical Cytology2026
A Unified Deep Learning Framework for Instance Segmentation Across Diverse Cytological Stains.
Article in Cytopathology : official journal of the British Society for Clinical Cytology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveAssess whether a single instance-segmentation model can operate robustly across multiple cytological stains, avoiding stain-specific pipelines without sacrificing accuracy.
methodsWe consolidated three expert-annotated datasets (Papanicolaou, Feulgen, AgNOR), standardised their formats and trained Mask R-CNN, Mask2Former, YOLO11n and YOLO12n under two regimes: per-stain and unified multi-stain. We reported AP
resultsFeulgen specialist models yielded the highest scores in our benchmark, with YOLO11n reaching AP
conclusionsA single multi-stain model is feasible for cytology instance segmentation, preserving detection and improving boundary accuracy in diverse stains. Transformer-based Mask2Former showed the best AP 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.
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