LKA: Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation

Deep learning models like Convolutional Neural Networks (CNNs) and Transformers have revolutionized image segmentation. While CNNs are computationally efficient due to their parameter-sharing mechanisms, transformers excel in capturing long-range data relationships and global context, at the cost of computational resources because of their self-attention layers. In this study, we evaluate an alternative approach that harnesses the benefits of both: a transformer architecture built exclusively with convolutions. We showcase that our model outperforms leading methods like nnUNet and Swin-UNETR in glioblastoma segmentation and provide evidence for the choice of architecture having control over models' texture bias.