Nucleus detection in histopathology whole slide images (WSIs) is crucial for a broad spectrum of clinical applications. The gigapixel size of WSIs necessitates the use of sliding window methodology for nucleus detection. However, mainstream methods process each sliding window independently, which overlooks broader contextual information and easily leads to inaccurate predictions. To address this limitation, recent studies additionally crop a large Filed-of-View (LFoV) patch centered on each sliding window to extract contextual features. However, such methods substantially increase whole-slide inference latency. In this work, we propose an effective and efficient context-aware nucleus detection approach. Specifically, instead of using LFoV patches, we aggregate contextual clues from off-the-shelf features of historically visited sliding windows, which greatly enhances the inference efficiency. Moreover, compared to LFoV patches used in previous works, the sliding window patches have higher magnification and provide finer-grained tissue details, thereby enhancing the classification accuracy. To develop the proposed context-aware model, we utilize annotated patches along with their surrounding unlabeled patches for training. Beyond exploiting high-level tissue context from these surrounding regions, we design a post-training strategy that leverages abundant unlabeled nucleus samples within them to enhance the model's context adaptability. Extensive experimental results on three challenging benchmarks demonstrate the superiority of our method.

翻译：组织病理学全切片图像（WSIs）中的细胞核检测对于广泛的临床应用至关重要。WSIs的千兆像素尺寸需要使用滑动窗口方法进行细胞核检测。然而，主流方法独立处理每个滑动窗口，忽略了更广泛的上下文信息，容易导致预测不准确。为解决这一局限，近期研究额外裁剪以每个滑动窗口为中心的大视场（LFoV）图像块以提取上下文特征。然而，此类方法显著增加了全切片推理延迟。在本工作中，我们提出了一种高效且有效的上下文感知细胞核检测方法。具体而言，我们不使用LFoV图像块，而是从历史访问滑动窗口的现成特征中聚合上下文线索，这极大地提升了推理效率。此外，与先前工作中使用的LFoV图像块相比，滑动窗口图像块具有更高的放大倍数，能提供更细粒度的组织细节，从而提高了分类准确性。为构建所提出的上下文感知模型，我们利用带标注的图像块及其周围未标注的图像块进行训练。除了从这些周围区域挖掘高层次的组织上下文信息外，我们还设计了一种后训练策略，利用其中丰富的未标注细胞核样本来增强模型的上下文适应能力。在三个具有挑战性的基准数据集上的大量实验结果证明了我们方法的优越性。