Bibliographic Details
| Title: |
Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images |
| Authors: |
Shui, Zhongyi, Guo, Ruizhe, Li, Honglin, Sun, Yuxuan, Zhang, Yunlong, Zhu, Chenglu, Cai, Jiatong, Chen, Pingyi, Su, Yanzhou, Yang, Lin |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
Nucleus detection in histopathology whole slide images (WSIs) is crucial for a broad spectrum of clinical applications. Current approaches for nucleus detection in gigapixel WSIs utilize a sliding window methodology, which overlooks boarder contextual information (eg, tissue structure) and easily leads to inaccurate predictions. To address this problem, recent studies additionally crops a large Filed-of-View (FoV) region around each sliding window to extract contextual features. However, such methods substantially increases the inference latency. In this paper, we propose an effective and efficient context-aware nucleus detection algorithm. Specifically, instead of leveraging large FoV regions, we aggregate contextual clues from off-the-shelf features of historically visited sliding windows. This design greatly reduces computational overhead. Moreover, compared to large FoV regions at a low magnification, the sliding window patches have higher magnification and provide finer-grained tissue details, thereby enhancing the detection accuracy. To further improve the efficiency, we propose a grid pooling technique to compress dense feature maps of each patch into a few contextual tokens. Finally, we craft OCELOT-seg, the first benchmark dedicated to context-aware nucleus instance segmentation. Code, dataset, and model checkpoints will be available at https://github.com/windygoo/PathContext.
Comment: under review |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2503.05678 |
| Accession Number: |
edsarx.2503.05678 |
| Database: |
arXiv |
