SegTME-UNI2 pairs a UNI2-based dual-head segmentation model trained via progressive pseudo-labeling with an LLM to produce multiclass cell maps and narrative TME descriptions from H&E images.
Hover-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-tissue Histology Images,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Dynamic Focal Attention learns class-specific difficulty via per-class biases in attention logits, improving Dice and IoU on imbalanced histopathology segmentation benchmarks.
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.
MIDOG 2025 challenge shows top mitosis detection F1 of 0.740 and atypical figure balanced accuracy of 0.908 across diverse tumors, with clear drops in challenging regions and tumor-type variation.
citing papers explorer
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SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology
SegTME-UNI2 pairs a UNI2-based dual-head segmentation model trained via progressive pseudo-labeling with an LLM to produce multiclass cell maps and narrative TME descriptions from H&E images.
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Learning Class Difficulty in Imbalanced Histopathology Segmentation via Dynamic Focal Attention
Dynamic Focal Attention learns class-specific difficulty via per-class biases in attention logits, improving Dice and IoU on imbalanced histopathology segmentation benchmarks.
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CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H&E Whole-Slide Images
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.
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Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge
MIDOG 2025 challenge shows top mitosis detection F1 of 0.740 and atypical figure balanced accuracy of 0.908 across diverse tumors, with clear drops in challenging regions and tumor-type variation.