Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images
This addresses a domain-specific problem for medical imaging researchers and practitioners by improving efficiency and accuracy in nucleus detection, though it is incremental as it builds on existing sliding window methods.
The paper tackles the problem of inefficient and inaccurate nucleus detection in histopathology whole slide images by proposing a context-aware algorithm that aggregates features from historically visited sliding windows, reducing computational overhead and improving detection accuracy, with results including a new benchmark and available code.
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.