HMDA: A Hybrid Model With Multi-Scale Deformable Attention for Medical Image Segmentation

Transformers have been applied to medical image segmentation tasks owing to their excellent long-range modeling capability, compensating for the failure of Convolutional Neural Networks (CNNs) to extract global features. However, the standardized self-attention modules in Transformers, characterized by a uniform and inflexible pattern of attention distribution, frequently lead to unnecessary computational redundancy with high-dimensional data, consequently impeding the model's capacity for precise concentration on salient image regions. Additionally, achieving effective explicit interaction between the spatially detailed features captured by CNNs and the long-range contextual features provided by Transformers remains challenging. In this architecture, we propose a Hybrid Transformer and CNN architecture with Multi-scale Deformable Attention(HMDA), designed to address the aforementioned issues effectively. Specifically, we introduce a Multi-scale Spatially Adaptive Deformable Attention (MSADA) mechanism, which attends to a small set of key sampling points around a reference within the multi-scale features, to achieve better performance. In addition, we propose the Cross Attention Bridge (CAB) module, which integrates multi-scale transformer and local features through channel-wise cross attention enriching feature synthesis. HMDA is validated on multiple datasets, and the results demonstrate the effectiveness of our approach, which achieves competitive results compared to the previous methods.