CoP achieves over 90% of per-instance SAM performance on cell-type benchmarks with one click per type via recursive non-parametric expansion of reliable same-type points.
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4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
CellDETR is a detection-guided framework extending Deformable DETR for cell representation learning from WSIs, with contrastive pretraining and cross-dataset transfer shown on PanNuke and Xenium data.
CellPrior-Net integrates hematoxylin channel prior into a lightweight CNN for nuclei detection and classification in H&E WSIs, claiming comparable accuracy to SOTA with significantly reduced inference time across 10.4M nuclei from diverse datasets.
A decoupled watershed-plus-EfficientNet pipeline recovers 75.95% of cells without annotations and reaches 98.36% stage classification accuracy with instance-level explainability on the NIH BBBC041 dataset.
citing papers explorer
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MalariAI: A Label-Resilient Decoupled Framework for Universal Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears
A decoupled watershed-plus-EfficientNet pipeline recovers 75.95% of cells without annotations and reaches 98.36% stage classification accuracy with instance-level explainability on the NIH BBBC041 dataset.