DA-RefineNet:A Dual Input Whole Slide Image Segmentation Algorithm Based on Attention
This work addresses the problem of efficient and accurate medical image segmentation for disease diagnosis, representing an incremental improvement over existing sliding window techniques.
The paper tackles the challenge of whole slide image segmentation by proposing DA-RefineNet, a dual-input attention network that leverages both local and global information, achieving better performance compared to single-input methods.
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.