通过自适应融合组织先验信息，提升病理图像细胞检测的准确性。
DualGate-Net: A Prior-Gated Dual-Encoder Framework for Histopathology Cell Detection
- 双编码器结构结合局部与全局特征，用可学习门控机制调节先验影响。
- 在OCELOT数据集上验证集和测试集的宏平均F1分别达0.7722和0.7345。
- 适合关注医学图像分析中上下文建模与鲁棒定位的研究者。
病理图像中的细胞检测高度依赖周围组织环境，同一类细胞在不同微环境下可能表现不同。现有组织感知方法虽引入上下文先验，但多采用静态融合策略，易传播噪声信息。本文提出DualGate-Net，一种基于先验感知的双编码器框架，结合基于ConvNeXtV2的局部编码器与基于SegFormer的全局编码器，通过可学习的先验门控融合模块，自适应调节各空间位置的组织先验影响。辅助的前景重建分支在训练中保留高频率细胞结构，细胞度引导线索进一步提升定位鲁棒性。在OCELOT基准测试上，验证集与测试集的宏平均F1分数分别达到0.7722和0.7345，证明了自适应先验融合对病理图像细胞检测的有效性。
原文摘要 · Abstract (English)
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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