Multi-Attention Feature Fusion for Nuclei Segmentation and Classification

Nucleus instance segmentation is crucial in the digital pathology, serving as a foundational step for subsequent tasks like precision medicine and cancer prognosis. In this paper, we introduce MAP-SegNet, a new network architecture designed for the segmentation and classification of nuclei in H&E stained images from multiple tissues. The key innovation of MAP-SegNet lies in its Multi-Attention Feature Fusion Module, which enhances the network's ability to efficiently combine multi-scale feature aggregation mechanisms with local and global features. The core component is the dual attention fusion module, which possesses the capability to adjust spatial attention weights dynamically by the input contextual information. This functionality enables the model to concentrate on various image regions, effectively capture long-range dependencies, and mitigate the risk of information loss. MAP-SegNet's uplifting accuracy in nuclei segmentation and classification is validated by rigorous quantitative experiments on the standard PanNuKe dataset.