DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps Segmentation

Polyps segmentation poses a significant challenge in medical imaging due to the flat surface of polyps and their texture similarity to surrounding tissues. This similarity gives rise to difficulties in establishing a clear boundary between polyps and the surrounding mucosa, leading to complications such as local overexposure and the presence of bright spot reflections in imaging. To counter this problem, we propose a new dual graph convolution network (Dual-GCN) and location self-attention mechanisms with weighted fast normalization fusion model, named DSFNet. First, we introduce a feature enhancement block module based on Dual-GCN module to enhance local spatial and structural information extraction with fine granularity. Second, we introduce a location fused self-attention module to enhance the model's awareness and capacity to capture global information. Finally, the weighted fast normalized fusion method with trainable weights is introduced to efficiently integrate the feature maps from encoder, bottleneck, and decoder, thus promoting information transmission and facilitating the semantic consistency. Experimental results show that the proposed model surpasses other state-of-the-art models in gold standard indicators, such as Dice, MAE, and IoU. Both quantitative and qualitative analysis indicate that the proposed model demonstrates exceptional capability in polyps segmentation and has great potential clinical significance. We have shared our code on anonymous website for evaluation.