Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images
Journal:
arXiv
Published Date:
Mar 4, 2025
Abstract
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.
