ABSTRACT
Attention mechanism-based medical image segmentation methods have developed rapidly recently. For the attention mechanisms, it is crucial to accurately capture the distribution weights of the effective features contained in the data. To accomplish this task, most attention mechanisms prefer using the global squeezing approach. However, it will lead to a problem of over-focusing on the global most salient effective features of the region of interest, while suppressing the secondary salient ones. Making partial fine-grained features are abandoned directly. To address this issue, we propose to use a multiple-local perception method to aggregate global effective features, and design a fine-grained medical image segmentation network, named FSA-Net. This network consists of two key components: 1) the novel Separable Attention Mechanisms which replace global squeezing with local squeezing to release the suppressed secondary salient effective features. 2) a Multi-Attention Aggregator (MAA) which can fuse multi-level attention to efficiently aggregate task-relevant semantic information. We conduct extensive experimental evaluations on five publicly available medical image segmentation datasets: MoNuSeg, COVID-19-CT100, GlaS, CVC-ClinicDB, ISIC2018, and DRIVE datasets. Experimental results show that FSA-Net outperforms state-of-the-art methods in medical image segmentation.