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

arXiv · 2308.07251

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

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Abstract

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 .

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Liam Chalcroft, Ruben Lourenço Pereira, Mikael Brudfors, Andrew S. Kayser, Mark D'Esposito, Cathy J. Price, Ioannis Pappas, John Ashburner. 2023-08-14. Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation. https://arxiv.org/abs/2308.07251

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