Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation

Liam Chalcroft, Ruben Lourenço Pereira, Mikael Brudfors, Andrew S. Kayser, Mark D'Esposito +3 more
8/14/2023
eess.IVcs.CV

Official code: liamchalcroft/MDUNet (6 stars, Apache-2.0, last commit 2023-12-16).

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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Code Implementations(1)

liamchalcroft/MDUNetOfficial100%
Apache-2.0

U-Net model using matrix decomposition framework proposed in Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation (https://arxiv.org/abs/2308.07251)

62Shell, PythonMay 22, 20222 years agoApache-2.0

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Cite this paper

@article{chalcroft2023largekernel,
  title  = {Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation},
  author = {Liam Chalcroft and Ruben Lourenço Pereira and Mikael Brudfors and Andrew S. Kayser and Mark D'Esposito and Cathy J. Price and Ioannis Pappas and John Ashburner},
  year   = {2023},
  eprint = {2308.07251},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.07251v1}
}

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