AnnotateAnyCell: Open-Source AI Framework for Efficient Annotation in Digital Pathology

A bstract Manual annotation of histopathological whole slide images remains a critical bottleneck for computational pathology and clinical AI deployment, requiring prohibitive expert time at scale. Here we present an open-source semi-supervised framework combining active contrastive learning with iterative human-in-the-loop feedback for efficient cellular annotation and classification. The pipeline integrates Cellpose segmentation, UMAP-based latent space visualization, and contrastive learning with pseudolabel propagation, evaluated on five whole slide images of canine invasive urothelial carcinoma across low, intermediate, and high histological grades at 40× magnification. Latent space clustering-guided annotation required 47 minutes compared to 63 minutes for sequential annotation, a 25% reduction (95% CI 18–32%). Classification accuracy reached 96.3% ± 1.2% for mitotic figures and 98.3% ± 1.4% for nucleoli using 1,075 labeled samples, with nucleoli classification achieving 95.5% ± 1.5% accuracy from only 215 samples. Inter-annotator agreement was high for chromatin ( κ = 1.00) and nucleoli ( κ = 0.95) but moderate for mitotic figures ( κ = 0.58) and nuclear shape ( κ = 0.36), reflecting intrinsic morphological ambiguity in these categories. This framework substantially reduces annotation burden while achieving expert-level accuracy for well-defined morphological features, providing a scalable path toward AI-assisted diagnostics in resource-constrained pathology settings.