A Reliable Segmentation Model for Limited Medical Image Datasets With Attention Mechanism and Kolmogorov–Arnold Networks

The effectiveness and robustness of medical image segmentation algorithms inextricably depends on the quality and quantity of training data. Insufficient data continually restricts model training and affects segmentation accuracy and robustness. We rigorously create a reliable image segmentation model to tackle the challenges presented by constrained medical image datasets. Our method integrates two novel components: the coordinate attention and identity (CAI) module and the Kolmogorov–Arnold and squeeze-and-excitation (KASE) module. The CAI module consists of two branches that enhance feature representation. The initial branch utilizes coordinate attention techniques to detect cross-channel dependency. This offers location-specific and directionally aware information crucial for precisely identifying target areas. The second branch utilizes features produced by convolution as identity mappings. This tackles problems such as vanishing and exploding gradients while maintaining essential features and semantic information. The KASE module comprises two branches. The initial branch improves weight calculations by extracting global context through multiscale squeeze-and-excitation methods. The other branch integrates Kolmogorov–Arnold networks with convolutional operations. This enhances the capacity of the proposed model to learn and represent complex features. These two modules enhance the robustness of the model and improves its generalization across varied input data. Finally, we train multiple state-of-the-art models on multiple datasets using 100%, 20%, 10%, and 5% of the training set in each dataset. The test results show that our model achieves competitive performance despite limited training data.