StarMA Net: A star-shape multi-scale attention network for medical imaging classification

Medical image classification is crucial for clinical diagnosis. However, medical datasets often face challenges such as limited characterization capabilities, difficulties in category differentiation, and the presence of individual differences. Although attention mechanisms can enhance feature representation, existing methods often struggle to utilize spatial information effectively and lack modeling of inter-channel interactions. We propose star-shaped multi-scale attention (StarMA). (1) It retains spatial information along different orientations within each channel through axial decomposition, enhancing the model's perception of complex structures. (2) A star-shaped structure is employed to project feature maps into a high-dimensional nonlinear space, strengthening inter-channel interactions and improving inter-class discriminability. (3) A cross-spatial aggregation learning strategy is introduced to integrate multi-scale contextual information, improving the model's ability to handle intra-class variability. Based on StarMA, we developed StarMA Net and conducted comparative experiments on five different datasets. Compared to advanced algorithms, StarMA Net demonstrates better effectiveness and robustness.