Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation

Understand

Vision transformers are effective deep learning models for vision tasks, including medical image segmentation.

• However, they lack efficiency and translational invariance, unlike convolutional neural networks (CNNs).
• To model long-range interactions in 3D brain lesion segmentation, we propose an all-convolutional transformer block variant of the U-Net architecture.
• We demonstrate that our model provides the greatest compromise in three factors: performance competitive with the state-of-the-art; parameter efficiency of a CNN; and the favourable inductive biases of a transformer.