Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images

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.

Authors

• Zhongyi Shui Zhejiang University Westlake University
• Honglin Li Innovation Center for Artificial Intelligence and Drug Discovery, East China Normal University, Shanghai, China
• Yunlong Zhang College of Computer Science and Technology, Zhejiang University, China Research Center for Industries of the Future and School of Engineering, Westlake University, China
• Yuxuan Sun College of Computer Science and Technology, Zhejiang University, China Research Center for Industries of the Future and School of Engineering, Westlake University, China
• Yiwen Ye National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China
• Pingyi Chen Zhejiang University Westlake University
• Ruizhe Guo Zhejiang University
• Lei Cui Microsoft Corporation
• Chenglu Zhu Westlake University
• Lin Yang

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