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arXiv · 2606.07222

DualGate-Net: A Prior-Gated Dual-Encoder Framework for Histopathology Cell Detection

Bahman Jafari Tabaghsar·Son Tran·K. Devaraja·Atul Sajjanhar
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Abstract

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.

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Bahman Jafari Tabaghsar, Son Tran, K. Devaraja, Atul Sajjanhar. 2026-06-05. DualGate-Net: A Prior-Gated Dual-Encoder Framework for Histopathology Cell Detection. https://arxiv.org/abs/2606.07222

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