Large-kernel Attention for Efficient and Robust Brain Lesion
  Segmentation
Liam Chalcroft Ruben Lourenço Pereira Mikael Brudfors Andrew S. Kayser Mark D'Esposito Cathy J. Price Ioannis Pappas John Ashburner
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. Our public
implementation is available at https://github.com/liamchalcroft/MDUNet .