FBDA-Net: Medical Image Segmentation via Foreground–Background Decoupled Attention Aggregation

Abstract

Medical image segmentation is a fundamental task in computer-aided diagnosis, playing an important role in organ measurement, lesion delineation, and surgical planning. However, due to the limitations of imaging conditions and the complexity of tissue characteristics, foreground targets and the background often exhibit a “soft boundary,” and the two are easily confused in ambiguous transition regions, which limits further improvement of segmentation accuracy. To address this, we propose FBDA-Net, a segmentation network based on foreground–background decoupled attention (FBDA). Its core module, the FBDA, decouples the input features into foreground and background at the feature level using prior masks, computes attention with a shared Query and three independent sets of Key/Value, and employs a joint Softmax so that the foreground, background, and global branches competitively allocate weights within the same probability space. Building upon this, we construct a top-down progressive decoupling decoder that converts the prediction at each level into prior masks and passes them downward layer by layer, driving the decoupled priors to be progressively reconstructed and refined along the decoding process, and we adopt a combinatorial supervision loss to strengthen the cross-level consistency of multi-level predictions. Experiments on three benchmark datasets—Synapse multi-organ CT, ACDC cardiac MRI, and ISIC 2016 dermoscopy—demonstrate competitive overall segmentation performance. Zero-shot external evaluation on PH2 without fine-tuning further supports the cross-dataset generalization of FBDA-Net. The source code is publicly available at https://github.com/TongLiuLDU/FBDANet.git.