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.
CellViT: Vision transformers for precise cell segmentation and classification.Medical Image Analysis, 94: 103143, 2024
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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.
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One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentation
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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CellDETR: A Detection-Guided Framework for Scalable Cell Representation Learning from Histopathology Images
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.