  Automatic medical image segmentation has wide applications for disease
diagnosing. However, it is much more challenging than natural optical image
segmentation due to the high-resolution of medical images and the corresponding
huge computation cost. The sliding window is a commonly used technique for
whole slide image (WSI) segmentation, however, for these methods based on the
sliding window, the main drawback is lacking global contextual information for
supervision. In this paper, we propose a dual-inputs attention network (denoted
as DA-RefineNet) for WSI segmentation, where both local fine-grained
information and global coarse information can be efficiently utilized.
Sufficient comparative experiments are conducted to evaluate the effectiveness
of the proposed method, the results prove that the proposed method can achieve
better performance on WSI segmentation compared to methods relying on
single-input.
