BiFUSE-UNet: a novel dual-encoder enhanced TransUNet for polyp segmentation

Polyps are abnormal protrusions that develop on the mucosal surface of the human body, arising from hyperproliferation or aberrant differentiation of mucosal tissue. While some polyps carry a risk of malignant transformation, timely intervention can halt this progression. Accurately segmenting polyp contours in medical images poses significant challenges, primarily due to the high heterogeneity in polyp appearance and morphology, blurry boundaries with background interference, and class imbalance coupled with data scarcity. To address these challenges, this paper presents BiFuse-UNet, a novel dual-encoder TransUNet architecture built upon a bidirectional fusion mechanism. Building upon the original TransUNet architecture, BiFuse-UNet incorporates several key modules. Firstly, a multi-axis attention module (MaxViT block) is integrated to form a dual-encoder structure, which extracts multi-scale local textural features across different dimensions and captures long-range dependencies through self-attention. Secondly, a Merge fusion module is designed, incorporating a Coordinate Attention module and the SaE block from SENetV2, to enhance the model's perception of local and detailed features. Finally, a Biformer module is introduced to strike an efficient balance between global semantics and local details, thereby improving the segmentation accuracy for subtle polyps and early-stage polyps with atypical morphology. Extensive evaluations on the Kvasir-SEG and CVC-ClinicDB dataset demonstrate that BiFuse-UNet achieves state-of-the-art segmentation performance, outperforming seven existing methods on key metrics such as the Dice coefficient, Recall, Precision, and IoU. The proposed model effectively mitigates the challenges of morphological heterogeneity, blurry boundaries, and background interference, leading to highly accurate pixel-level segmentation.