DualGate-Net: A Prior-Gated Dual-Encoder Framework for Histopathology Cell Detection
This work improves cell detection in histopathology by adaptively integrating tissue context, addressing a key challenge for pathologists analyzing tissue microenvironments.
DualGate-Net introduces a prior-gated dual-encoder framework for histopathology cell detection that adaptively fuses local and global features, achieving macro F1-scores of 0.7722 on validation and 0.7345 on test sets on the OCELOT benchmark.
Cell detection in histopathology images strongly depends on surrounding tissue context, where visually similar cells may belong to different classes under different microenvironments. Recent tissue-aware methods incorporate contextual priors, but often rely on static fusion strategies that may propagate noisy information. In this work, we propose DualGate-Net, a prior-aware dual-encoder framework that combines a ConvNeXtV2-based local encoder and a SegFormer-based global encoder through a learnable prior-gated fusion mechanism. The proposed module adaptively regulates the influence of tissue priors across spatial locations, while an auxiliary foreground reconstruction branch preserves high-frequency cellular structures during training. In addition, auxiliary cellness-guided cues are incorporated to further improve localization robustness. Experiments on the OCELOT benchmark demonstrate consistent improvements, achieving macro F1-scores of 0.7722 on the validation set and 0.7345 on the test set, highlighting the effectiveness of adaptive prior integration for robust histopathology cell detection.