Can I read Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation on EtoBox?

Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation by Chalcroft, Liam; Pereira, Ruben Lourenço; Brudfors, Mikael; Kayser, Andrew S.; D'Esposito, Mark; Price, Cathy J.; Pappas, Ioannis; Ashburner, John is a scholarly article available to read on EtoBox.

What is Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation about?

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 .

Author

Chalcroft, Liam; Pereira, Ruben Lourenço; Brudfors, Mikael; Kayser, Andrew S.; D'Esposito, Mark; Price, Cathy J.; Pappas, Ioannis; Ashburner, John

Published

2023

Language

EN